All - O'Reilly Mediahttps://www.oreilly.com2019-01-21T21:51:16ZAll of our Ideas and Learning material from all of our topics.O'Reilly Media38.393314-122.836667oreilly/radar/atomhttps://feedburner.google.comThis is an XML content feed. It is intended to be viewed in a newsreader or syndicated to another site.Four short links: 21 January 20192019-01-21T13:05:00Ztag:www.oreilly.com,2019-01-21:/ideas/four-short-links-21-january-2019<p><em>Programming Spreadsheets, Star Emulator, AI for Social Good, Participatory Democracy</em></p><ol>
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<a href="https://www.microsoft.com/en-us/research/blog/influencing-mainstream-software-applying-programming-language-research-ideas-to-transform-spreadsheets/">Applying Programming Language Research Ideas to Transform Spreadsheets</a> (Microsoft) -- <i>an Excel cell can now contain a first-class record, linked to external data sources. And ordinary Excel formulas can now compute array values, that “spill” into adjacent cells (dynamic arrays). There is more to come: we have a working version of full, higher-order, lexically scoped lambdas (and let-expressions) in Excel’s formula language and we are prototyping sheet-defined functions and full nesting of array values.</i>
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<a href="https://engblg.livingcomputers.org/index.php/2019/01/19/introducing-darkstar-a-xerox-star-emulator/">Darkstar: A Xerox Star Emulator</a> -- this blog post describes the journey of building <a href="https://github.com/livingcomputermuseum/Darkstar">the emulator</a> for this historic system. From the good folks at the Living Computer Museum in Seattle.</li>
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<a href="https://ai.google/social-good/impact-challenge">AI for Social Good Impact Challenge</a> -- $25M pool, $500K-$2M for 1-3 years. <i>If you are selected to receive a grant, the standard grant agreement will require any intellectual property created with grant funding from Google be made available for free to the public under a permissive open source license</i>.</li>
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<a href="https://decidim.org/">Decidim</a> -- <i>free open source participatory democracy for cities and organizations.</i>
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<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-21-january-2019'>Four short links: 21 January 2019.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/yiSFH4H0aQM" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/rDjjDjz3tOQ" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-21-january-2019http://feedproxy.google.com/~r/oreilly/radar/atom/~3/yiSFH4H0aQM/four-short-links-21-january-2019Four short links: 18 January 20192019-01-18T12:00:00Ztag:www.oreilly.com,2019-01-18:/ideas/four-short-links-18-january-2019<p><em>Remove Filters, Quantum Cables, Embedded Vision, and Citizen Developers</em></p><ol>
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<a href="https://github.com/ipsingh06/ml-desnapify">Desnapify</a> -- <i>deep convolutional generative adversarial network (DCGAN) trained to remove Snapchat filters from selfie images.</i>
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<a href="https://www.technologyreview.com/s/612760/quantum-computers-component-shortage/">Quantum Computer Component Shortage</a> (MIT TR) -- cables for superconducting quantum computing experiments turn out to be hard to find at Radio Shack. Reminder: QC is in its infancy.</li>
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<a href="https://github.com/symisc/sod">SOD</a> -- <i>an embedded computer vision and machine learning library (CPU optimized and IoT capable)</i>.</li>
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<a href="http://blog.eladgil.com/2019/01/interesting-markets-2019-edition.html">Devsumer</a> -- interesting argument: lots of people with exposure to programming via Hour of Code type things, as IT departments are too busy to build all the apps people want, so <i>[a] number of products have emerged that allow people to build simple software applications, or to use templated applications for their own work flow or productivity. You can think of this as taking a SQL database or excel spreadsheet and turning it into an app platform.</i>
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<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-18-january-2019'>Four short links: 18 January 2019.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/57nDGsGgM7I" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/dND5XInGh0Q" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-18-january-2019http://feedproxy.google.com/~r/oreilly/radar/atom/~3/57nDGsGgM7I/four-short-links-18-january-2019How machine learning impacts information security2019-01-17T13:50:00Ztag:www.oreilly.com,2019-01-17:/ideas/how-machine-learning-impacts-information-security<p><img src='https://d3ucjech6zwjp8.cloudfront.net/600x450/hacker-1944688_crop-b34a76e3cab9c07c5900b706c70a12c3.jpg'/></p><p><em>The O’Reilly Data Show Podcast: Andrew Burt on the need to modernize data protection tools and strategies.</em></p><p>In this episode of the <a href="https://www.oreilly.com/ideas/topics/oreilly-data-show-podcast">Data Show</a>, I spoke with <a href="https://www.linkedin.com/in/andrew-burt/">Andrew Burt</a>, chief privacy officer and legal engineer at <a href="https://www.immuta.com/">Immuta</a>, a company building data management tools tuned for data science. Burt and cybersecurity pioneer Daniel Geer recently released a must-read <a href="https://www.hoover.org/research/flat-light">white paper (“Flat Light”)</a> that provides a great framework for how to think about information security in the age of big data and AI. They list important changes to the information landscape and offer suggestions on how to alleviate some of the new risks introduced by the rise of machine learning and AI. </p>
<p>We discussed their new white paper, cybersecurity (Burt was previously a special advisor at the FBI), and an exciting <a href="https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/72803">new Strata Data tutorial</a> that Burt will be co-teaching in March.</p>
<p>Continue reading <a href='https://www.oreilly.com/ideas/how-machine-learning-impacts-information-security'>How machine learning impacts information security.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/qcZPEPdbXIA" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/XYPXSuJ1bSI" height="1" width="1" alt=""/>Ben Loricahttps://www.oreilly.com/ideas/how-machine-learning-impacts-information-securityhttp://feedproxy.google.com/~r/oreilly/radar/atom/~3/qcZPEPdbXIA/how-machine-learning-impacts-information-securityFour short links: 17 January 20192019-01-17T12:00:00Ztag:www.oreilly.com,2019-01-17:/ideas/four-short-links-17-january-2019<p><em>Git, SMS Deep Dive, Rethinking Capitalism, and Polarized Opinions</em></p><ol>
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<a href="https://github.com/pwoolcoc/pushb">pushb</a> -- <i>like pushd/popd, but for git branches.</i> See also "<a href="https://rachelcarmena.github.io/2018/12/12/how-to-teach-git.html">How to Teach Git</a>," Rachel Carmena's visual explanation of the mental models of directories that will help new git users.</li>
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<a href="https://scottbot.net/the-route-of-a-text-message/">The Route of a Text Message</a> (Scott Weingart) -- <i>a single text message: how it was typed, stored, sent, received, and displayed. I sprinkle in some history and context to break up the alphabet soup of protocols, but though the piece gets technical, it should all be easily understood.</i>
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<a href="https://www.youtube.com/watch?v=qSt5suImn78">The Market-Shaping Forces of Capitalism</a> (Mariana Mazzucato) -- YouTube video of her first lecture on the "Rethinking Capitalism" undergraduate module at UCL. Tim's a big fan of her work.</li>
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<a href="https://github.com/nukeop/nuclear">nuclear</a> -- describes itself as "Popcorn Time for music," but far more interesting for this fantastic line: <i>highly polarized opinions about languages and frameworks are characteristic of people who lack real-world programming experience and are more interested in building an identity than creating computer programs.</i>
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<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-17-january-2019'>Four short links: 17 January 2019.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/C-Xlq_9InTU" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/hT-bMdlOpOc" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-17-january-2019http://feedproxy.google.com/~r/oreilly/radar/atom/~3/C-Xlq_9InTU/four-short-links-17-january-2019What lies ahead for Python, Java, Go, C#, Kotlin, and Rust2019-01-17T11:00:00Ztag:www.oreilly.com,2019-01-17:/ideas/what-lies-ahead-for-python-java-go-c-kotlin-and-rust<p><img src='https://d3ucjech6zwjp8.cloudfront.net/600x450/soft-eng-sandstone-crop-dbf063d481ec638ff7114e2ae493157c.jpg'/></p><p><em>O’Reilly authors and instructors explore the near-term future of popular and growing programming languages.</em></p><p>Change is the only constant in the technology world, and programming languages are no exception. Competition among languages has led to improvements across the board. Established players like Java have added major features, while upstart languages like Go and Rust look to improve packaging and exception handling to add “fit and finish” to their ecosystems. As we enter 2019, we asked some of our O’Reilly authors and training course instructors for their thoughts on what’s in store for established players and fast-growing languages.</p>
<h2>Python</h2>
<p>Python's incredible growth over the past decade shows no signs of slowing. In addition to maintaining its position as the most popular introductory language for students, scientists, and knowledge workers, Python will continue its widespread adoption in web development, DevOps, data analysis, and machine learning circles. Matt Harrison, who runs the Python and data science training and consulting company MetaSnake (and is <a href="https://www.safaribooksonline.com/search/?query=matt%20harrison&amp;extended_publisher_data=true&amp;highlight=true&amp;is_academic_institution_account=false&amp;source=user&amp;include_assessments=false&amp;include_case_studies=true&amp;include_courses=true&amp;include_orioles=true&amp;include_playlists=true&amp;formats=live%20online%20training&amp;publishers=O%27Reilly%20Media%2C%20Inc.&amp;sort=date_added">a frequent instructor of Python courses on the O'Reilly online learning platform</a>), offers his take:</p>
<blockquote>
<p>Python has traditionally been more focused on small data, but I think that as other tools that enable big data—such as Dask and flexible Python solutions on top of Kubernetes—continue to improve, we will see Python dominate in big data as well. I’m continuing to see large companies that have traditionally used Java or proprietary languages replacing those with Python.</p>
</blockquote>
<p>In 2019, the Python community will cohere around Python 3, as <a href="https://pythonclock.org/">maintenance for Python 2 will end on January 1, 2020</a>. And it will do so under a new governance model, as Guido van Rossum, the creator of the language, stepped down as "benevolent dictator for life" in July 2018. After months of debate, the community recently voted to go forward under a <a href="https://discuss.python.org/t/python-governance-vote-december-2018-results/546">steering council model</a>.</p>
<h2>Java</h2>
<p>The release of Java 11 in September introduced major new features, such as nest-based access controls, which eliminate the need for compilers to insert bridge methods; dynamic class-file constraints; the new HttpClient, which removes the need for an external dependency when writing applications to communicate with web services; and the adoption of the Unicode 10 standard for localization. As Ben Evans, coauthor of <a href="https://www.safaribooksonline.com/library/view/optimizing-java/9781492039259/"><em>Optimizing Java</em></a> and <a href="https://www.safaribooksonline.com/library/view/java-in-a/9781449371296/"><em>Java in a Nutshell</em></a>, explains: "Java has adapted well to new frontiers such as cloud and microservices. Java 8 had problems with microservice startup times, but Java 11 solves that problem. It's a much better environment for developing new microservice applications from scratch."</p>
<p>Looking ahead to future versions of Java, Evans says that bringing value types to Java is a major current project. Value types are intended to be a third form of data type (to complement the existing primitive types and object references), which Evans sees as one way to future-proof the JVM, calling it one of the major changes to the language that "will change the character of Java development in fundamental ways."</p>
<h2>Go</h2>
<p>The Go team is working on a prototype command called <code>vgo</code>. Currently, when you install third-party libraries with the <code>go get</code> tool, the latest available version of a package is retrieved, even if it includes incompatibilities that can break your code. The <code>vgo</code> tool will “help you manage the versions of the various packages your app requires, without introducing conflicts,” explains Jay McGavren, author of the forthcoming <a href="https://www.safaribooksonline.com/library/view/head-first-go/9781491969540/"><em>Head First Go</em></a>.</p>
<p>The late 2018 release of Go 1.11 provided experimental support for compiling Go to WebAssembly, a binary format for code that can run in a web browser. “This promises to be faster and more efficient than JavaScript,” McGavren says. “And it’s supported by all the major browsers. The ability to make apps using Go that can run inside the browser offers new possibilities that I’m excited to explore.”</p>
<h2>C#</h2>
<p>The upcoming release of C# 8.0 will include a number of <a href="https://blogs.msdn.microsoft.com/dotnet/2018/11/12/building-c-8-0/">new features</a>, notably nullable reference types. <a href="http://www.stellman-greene.com/">Andrew Stellman</a>, coauthor of <a href="https://www.safaribooksonline.com/library/view/head-first-c/9781449358846/"><em>Head First C#</em></a>, calls it “code safety for the rest of us,” as it causes the compiler to give warnings any time a reference type variable can potentially be assigned a null value, thus “giving developers a new way to write safer code.”</p>
<p>Stellman notes that another upcoming feature that has C# developers talking is asynchronous streams—<code>foreach await</code> is a new version of the familiar <code>foreach</code> keyword that will consume an asynchronous stream, represented by the <code>IAsyncEnumerable</code> interface, automatically pausing the loop until the next value is available. Other expected new features include an asynchronous version of yield return and asynchronous disposables.</p>
<h2>Kotlin</h2>
<p>Kotlin's latest release (Kotlin 1.3, released in late October 2018) saw coroutines—lightweight threads that allow code to scale-out efficiently—graduated from experimental to stable status. Coroutines enable the creation of multiple pieces of code that can run asynchronously; for example, you can launch a background job (such as reading data from an external server) without the rest of your code having to wait for the job to complete before doing anything else. “This gives users a more fluid experience, and it also makes your application more scalable,” says David Griffiths, coauthor (along with Dawn Griffiths) of the forthcoming <a href="https://www.safaribooksonline.com/library/view/head-first-kotlin/9781491996683/"><em>Head First Kotlin</em></a>. Coroutines are also at the heart of Ktor, a new framework for building asynchronous servers and clients in connected systems using the Kotlin language.</p>
<p>Looking ahead to 2019, Kotlin is “likely to see significant use beyond the Java world,” says Griffiths. “It is proving to be an excellent language for library builders. If you have an application that performs some complex financial calculation on the server, Kotlin allows you to convert that server code into a Kotlin library which can run on both the server and the client.” Also anticipated for Kotlin, according to Griffiths, are first-class immutability support for the language and features that reduce or eliminate shared mutable state in concurrent code.</p>
<h2>Rust</h2>
<p>Rust 2018, released in December, was the first major new edition of the language since Rust 1.0 in 2015. Rust 2018 introduced <code>async</code> (asynchronous) functions and await expressions in order to make Rust more effective for writing network servers and other I/O-intensive applications. “An <code>async</code> function can use an <code>await</code> expression to suspend its execution until the data it needs becomes available,” says Jim Blandy, coauthor of <a href="https://www.safaribooksonline.com/library/view/programming-rust/9781491927274/"><em>Programming Rust</em></a>. “Rust has supported asynchronous programming in one form or another for a long time,” he notes, “but <code>async</code> functions provide a syntax for this sort of code that is a major improvement over what Rust has had before.”</p>
<p>Another in-the-works enhancement to Rust is improvement of Rust’s existing support of the WebAssembly standard for running code as part of a web page. “This will make it easier to integrate WebAssembly packages written in Rust with the existing JavaScript ecosystem, ” says Blandy.</p>
<h2>What's next?</h2>
<p>“What’s next?” is the question that's always on every programmer’s mind. In 2019 and beyond, language design will continue to look for new ways to help programmers manage complexity and abstraction, as applications and data grow ever larger and become more crucial to the modern enterprise.</p>
<p>Continue reading <a href='https://www.oreilly.com/ideas/what-lies-ahead-for-python-java-go-c-kotlin-and-rust'>What lies ahead for Python, Java, Go, C#, Kotlin, and Rust.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/8qyv48sc6tE" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/es2Q0VibDHY" height="1" width="1" alt=""/>Tyler Ortman, Jeff Bleielhttps://www.oreilly.com/ideas/what-lies-ahead-for-python-java-go-c-kotlin-and-rusthttp://feedproxy.google.com/~r/oreilly/radar/atom/~3/8qyv48sc6tE/what-lies-ahead-for-python-java-go-c-kotlin-and-rustAI brings speed to security2019-01-16T14:40:00Ztag:www.oreilly.com,2019-01-16:/ideas/ai-brings-speed-to-security<p><img src='https://d3ucjech6zwjp8.cloudfront.net/600x450/light-1834289_1920_crop-a0414e537f57b81d2e36c7f2c9fff900.jpg'/></p><p><em>Survey results indicate incident response times improve with AI-based security services.</em></p><p>Organizations that use security tools with artificial intelligence (AI) and machine learning (ML) see a significant decrease in incident response time, according to a survey of 457 security practitioners conducted by O’Reilly Media in conjunction with Oracle.</p>
<p>Twenty percent of IT professionals who rely on traditional security measures said their teams can detect a malware infection or other attack within minutes, according to the survey. But among IT pros who reported using AI and ML security services, that number more than doubled to 45%. The long tail shows a similar trend: only 16% of IT professionals need days or longer to find an infection when AI or ML is involved, versus a whopping 35% for those who don’t use these technologies.</p>
<figure class="full" id="id-6YOix"><img alt="detect a malware infection or other attack" src="https://d3ansictanv2wj.cloudfront.net/Graph1-6b805193ac1cd4862e4e12d09aacc91a.png"></figure>
<p>As cyberattacks become more malicious and stealthy, it's increasingly important to improve incident response time in order to detect and mitigate threats before they unleash their full fury. Eighty-four percent of survey respondents who use ML and AI security services said their response times are within minutes or hours. Among respondents who don't use these technologies, that number was substantially lower: 66%.</p>
<figure class="full" id="id-RlWiz"><img alt="security response times" class="image" src="https://d3ansictanv2wj.cloudfront.net/Graph2-6ad4899938dc70609d7480dc5d62a33f.png"></figure>
<p>But AI and ML alone aren't responsible for this improvement in incident response time. Shorter response times were also associated with the use of security information and event management, antimalware, vulnerability scanning, and bot management software, according to the survey. It’s also worth noting that, because many vendors tout traditional business intelligence techniques as artificial intelligence, some respondents may have said they use the technology when they really use more traditional algorithms instead.</p>
<figure class="center" id="id-RN4iK"><img style="max-height: 100vh;" alt="use of security information and event management" src="https://d3ansictanv2wj.cloudfront.net/Graph3-2-8ecd98c369399083f6d6cd8c83214bf3.png"></figure>
<h2>AI security services still catching on</h2>
<p>Despite the improvements that AI and ML bring to incident response time, the survey showed that most organizations have not yet adopted the technologies. Just 26% of respondents have started to embrace ML and AI security services, and another 28% said they're interested in learning more about them.</p>
<figure class="full" id="id-5dEi7"><img alt="" class="image" src="https://d3ansictanv2wj.cloudfront.net/Graph4-3286d913b3e2a8d52641cae0f762d3f3.png"></figure>
<p>According to the survey report, we can expect increasing interest in AI-based security tools over the next few years, in the same way that AI is making its entry into other industries.</p>
<p>As rapid response times show, adoption may happen very quickly because it can be a useful differentiator between businesses that avoid crippling attacks and those that fall victim to them.</p>
<h2>To cloud or not to cloud?</h2>
<p>Surprisingly, 38% of respondents are still only using on-premises, stand-alone appliances. A significant proportion of IT professional are using only traditional tools for security and are missing the trend of more modern, scalable solutions.</p>
<p>As for the rest, 51% of respondents employ a combination of on-premises and cloud-based security tools, but just 9% use only cloud-based security services.</p>
<figure class="full" id="id-5bMiM"><img alt="" class="image" src="https://d3ansictanv2wj.cloudfront.net/Graph6-54661d709b3450c9bff27f266789f396.png"></figure>
<p>One of the reasons why so few professionals have embraced cloud cybersecurity solutions could be the concern of cloud breaches: the potential for data breaches is the top cybersecurity concern IT pros have about using the public cloud.</p>
<figure class="full" id="id-3aBiB"><img alt="" class="image" src="https://d3ansictanv2wj.cloudfront.net/Graph5-a4a00075bf11d903cb96cbce8d6aac78.png"></figure>
<h2>Security is integral to IT budgets for organizations with CISOs</h2>
<p>We asked respondents what percentage of their IT budget went to security. Of those who answered, the vast majority (79%) indicated they spend 10% or less of their IT budget on security.</p>
<figure class="full" id="id-5AEiz"><img alt="" class="image" src="https://d3ansictanv2wj.cloudfront.net/Graph8-65e212c44a6700ed171c4e705dc29cca.png"></figure>
<p>The results show the lowest category of expenditure (less than 5%) was the most frequently selected response amongst respondents reporting the responsibility for security lies with the director or VP of IT, CIO, or CEO (45%, 46%, and 44% of respondents, respectively).</p>
<p>In contrast, higher levels of spending were cited amongst respondents who reported the responsibility for security fell to the CISO (49% of respondents indicating a CISO also selected 5%-­10% spend).</p>
<figure class="center" id="id-3GLim"><img style="max-height: 100vh;" alt="" class="image" src="https://d3ansictanv2wj.cloudfront.net/Graph9-46e51dd2c428ac13acd4ed88dcabcd60.png"></figure>
<p>A smaller budget also means the least modern tools: respondents with the smallest security budgets (less than 5% going toward security) were more likely to deploy security tools only on-premises (49% of these sites, versus 23-26% of sites with higher budgets). This suggests people who move to the cloud are willing to spend more to protect security. We don't know whether this means cloud security tools are more expensive, that their clients care more about security, or that they feel they are more at risk in the cloud than on-premises.</p>
<figure class="center" id="id-RkAiJ"><img style="max-height: 100vh;" alt="" class="image" src="https://d3ansictanv2wj.cloudfront.net/Graph10-caf5a3e5cbd2f0fb82cdbe7fd58f12fb.png"></figure>
<h2>Additional findings</h2>
<p>The report also found the top tools and strategies used to preemptively mitigate attacks on websites and applications are vulnerability scans, privileged access management, network firewalls, and web application firewalls.</p>
<figure class="full" id="id-592iW"><img alt="" class="image" src="https://d3ansictanv2wj.cloudfront.net/Graph7-2-0e9efb113d699963d1d420af1211ddce.png"></figure>
<h2>About the respondents</h2>
<p>We asked the respondents to tell us a little about themselves and their organizations, and the results were similar to those for our <a href="https://www.oreilly.com/ideas/how-network-professionals-deal-with-attacks-and-disruptions">resilience survey</a>. For instance, organizational size was dramatically skewed to the smallest and largest: 40% of respondents work in organizations with 1-199 people, while 25% work in organizations with 10,000 or more.</p>
<figure class="full" id="id-RywiV"><img alt="" class="image" src="https://d3ansictanv2wj.cloudfront.net/Graph11-adf08aeb297b9ea3a26da971ea5d9766.png"></figure>
<p>The respondents answer to a wide range of job descriptions, from system administrators and network operations to upper management. And they come from a variety of industries, although two stand out: IT services takes 21% of the share of respondents, and software takes another 15%.</p>
<figure class="full" id="id-6rJia"><img alt="" class="image" src="https://d3ansictanv2wj.cloudfront.net/Graph12-b2fb95af4243a07613f904427a903c49.png"></figure>
<p><em>This post is a collaboration between O’Reilly and </em><a href="https://dyn.com/oracle/"><em>Oracle Dyn</em></a><em>. </em><a href="https://www.oreilly.com/about/editorial_independence.html"><em>See our statement of editorial independence</em></a><em>.</em></p>
<p>Continue reading <a href='https://www.oreilly.com/ideas/ai-brings-speed-to-security'>AI brings speed to security.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/h8u6bRLoAVs" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/6jiRpD0LBt0" height="1" width="1" alt=""/>Laurent Gilhttps://www.oreilly.com/ideas/ai-brings-speed-to-securityhttp://feedproxy.google.com/~r/oreilly/radar/atom/~3/h8u6bRLoAVs/ai-brings-speed-to-securityOvercoming barriers to AI adoption2019-01-16T12:40:00Ztag:www.oreilly.com,2019-01-16:/ideas/overcoming-barriers-to-ai-adoption<p><img src='https://d3ucjech6zwjp8.cloudfront.net/600x450/site-3823519_1920_crop-5fb0a01fc1c4d6c97dd61dbf43468d9c.jpg'/></p><p><em>The program for our Artificial Intelligence Conference in New York City will showcase tools, best practices, and use cases from companies leading the way in AI adoption.</em></p><p>In early 2018, we conducted <a href="https://www.oreilly.com/data/free/how-companies-are-putting-aI-to-work-through-deep-learning.csp">a survey</a> to gauge the rate of adoption of deep learning. We found a majority of respondents were planning to use deep learning in future projects. When asked what held back adoption of deep learning, the same set of respondents cited “lack of skilled people,” “data-related challenges,” and “compute resources” as the main obstacles they faced.</p>
<p>We conducted another survey at the end of 2018, this time aimed at understanding adoption patterns for a broader set of AI technologies (not just deep learning). We found that companies that are just getting started using AI (what we termed the “evaluation stage”) cited company culture and difficulties identifying appropriate use cases as barriers to adoption. In contrast, those with more experience using AI technologies (what we termed a “mature practice”) cited “lack of data” and “lack of skilled people” as their main challenges.</p>
<figure class="center" id="id-6YOix"><img alt="barriers to ai adoption" src="https://d3ansictanv2wj.cloudfront.net/Figure1-0a68cebf3a8a90c604272e620f484d3d.png"></figure>
<p>Much of the recent progress in research and tools is accessible to developers, and there are more instructional materials available as well. We are also beginning to see more case studies involving AI and automation technologies. Along with recent progress in models and algorithms, we’ll be showcasing tools, best practices, and use cases from leading companies at the <a href="https://conferences.oreilly.com/artificial-intelligence/ai-ny">Artificial Intelligence Conference in New York City</a>, April 15-18, 2019.</p>
<h2>Company culture and targeting the right use cases</h2>
<p>As I noted in a recent <a href="https://www.oreilly.com/ideas/assessing-progress-in-automation-technologies">post</a>, there are many areas where current AI and automation technologies can already make an impact. We’ve assembled a series of training, tutorials, and briefings—the <a href="https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/topic/2983">AI Business Summit</a>—designed to help managers and executives develop playbooks for how to integrate AI technologies into existing workflows and products. Current AI and machine learning technologies require large amounts of data, so it makes sense for companies to investigate use cases in areas where they have existing data applications. This is precisely what we found in our upcoming survey, “Artificial Intelligence in the Enterprise”: for example, respondents from the financial services sector were already using AI in “customer service” or “finance and accounting.”</p>
<figure class="center" id="id-RN4iK"><img alt="what parts of the company use AI projects" src="https://d3ansictanv2wj.cloudfront.net/Figure2-08702f5d7dd3d7c663e7251f85b6b29f.png"></figure>
<p>It’s also no surprise that even though deep learning is often associated with computer vision and speech technologies, we are beginning to see it used in areas where companies already have existing data sets and machine learning applications (specifically areas that involve text and time series). In fact, according to the survey results, structured data and text remain the main data types used for AI applications. One of the sessions at the conference <a href="https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/detail/74695">will explore BERT</a>, an exciting new language representation model that delivers state-of-the-art results in a wide range of natural language processing tasks.</p>
<p>Related training programs, tutorials, and sessions to explore at the Artificial Intelligence Conference in New York City include:</p>
<ul>
<li><a href="https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/topic/2983">AI Business Summit</a></li>
<li><a href="https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/stopic/3017">AI in the Enterprise</a></li>
<li><a href="https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/stopic/3015">Text, Language, and Speech</a></li>
<li><a href="https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/stopic/3014">Temporal data and time-series</a></li>
<li><a href="https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/stopic/3011">Computer Vision</a></li>
</ul>
<p>There have been numerous articles written about artificial general intelligence, but the reality is that, at least for now, many of the AI systems that have captured press coverage have very specific and narrow capabilities. Entrepreneurs have taken notice. Many interesting AI startups are working on applications that are domain specific and target particular tasks and workflows. At the Artificial Intelligence Conference, we’ll have sessions and case studies from many industries, including:</p>
<ul>
<li><a href="https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/stopic/3013">Financial Services</a></li>
<li><a href="https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/stopic/3019">Media, Marketing, Advertising</a></li>
<li><a href="https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/stopic/3020">Health and Medicine</a></li>
<li><a href="https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/stopic/3022">Retail and e-commerce</a></li>
</ul>
<h2>The skills gap and lack of data</h2>
<p>As I noted, respondents who work at organizations with more mature AI practices cited “data” and “lack of people” as the primary challenges they face as they attempt to adopt more AI technologies. When it comes to data, <a href="https://www.oreilly.com/ideas/assessing-progress-in-automation-technologies">there are new tools</a> designed to help companies overcome lack of data, including tools for generating synthetic data, simulation environments, and automation technologies designed to supercharge human labelers (<a href="https://aws.amazon.com/sagemaker/groundtruth/">Amazon Ground Truth</a> is the most recent example).</p>
<p>In our upcoming survey, “Artificial Intelligence in the Enterprise,” we found companies at different stages of AI adoption are in need of talent across many skill sets. In particular, the need for skilled data and infrastructure engineers is the same across maturity levels:</p>
<figure class="center" id="id-5bMiM"><img alt="ai skills gaps" src="https://d3ansictanv2wj.cloudfront.net/Figure3-80720e5ebe1c55574b9d577f3df75704.png"></figure>
<p>Improvements in tools have made AI more accessible for non-experts. Many machine learning libraries are open source, and many cutting-edge models (found in research papers) eventually get implemented in these libraries. In our <a href="https://www.oreilly.com/data/free/how-companies-are-putting-aI-to-work-through-deep-learning.csp">previous survey</a>, we found the top three deep learning tools to be TensorFlow (at the time, used by 61% of all respondents), Keras (25%), and PyTorch (20%). This year, we found a higher rate of usage for Keras (34%) and PyTorch (29%). We also are seeing improvements in tools for reinforcement learning: the most recent version of <a href="https://github.com/ray-project/ray">Ray</a> now <a href="https://rise.cs.berkeley.edu/blog/scaling-multi-agent-rl-with-rllib">supports multi-agent reinforcement learning</a> at large scale.</p>
<p>But AI requires a suite of technologies that go beyond machine learning libraries. To this end, companies are beginning to build or buy tools that can support and sustain their teams of data scientists and suites of AI applications. Leading companies will present some of their internal tools and platforms at the Artificial Intelligence Conference in April:</p>
<ul>
<li><a href="https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/stopic/3009">Deep Learning and Machine Learning tools</a></li>
<li><a href="https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/stopic/3016">Platforms and infrastructure</a></li>
<li><a href="https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/stopic/3008">Models and Methods</a></li>
<li><a href="https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/stopic/3010">Reinforcement Learning</a></li>
<li><a href="https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/stopic/3025">Data and Data Networks</a></li>
<li><a href="https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/detail/73785">“Open source tools for machine learning models and data sets versioning”</a></li>
</ul>
<h2>Responsible AI</h2>
<p>In parallel with the progress in AI research and the progress in tools for building AI applications, there has been strong awareness around issues that go beyond simply optimizing business or quantitative metrics. This includes such topics as ethics, privacy and security, fairness, reliability and safety, and the economic impact of automation technologies. The community is beginning to come up with concrete strategies and best practices to address many of these concerns. For example, <a href="https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/detail/73864">one of the sessions</a> at the conference will provide a technical overview for a <a href="https://ai.google/education/responsible-ai-practices">framework for Responsible AI</a> used within Google. Additionally, there will be many other practical sessions at the Artificial Intelligence Conference from practitioners who are working at the forefront of designing AI applications that can overcome many of these important considerations:</p>
<ul>
<li><a href="https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/stopic/3012">Ethics, Privacy, and Security</a></li>
<li><a href="https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/detail/73598">“Introducing the AI Fairness 360 Toolkit”</a></li>
<li><a href="https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/detail/73760">“How to build privacy and security into deep learning models”</a></li>
<li><a href="https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/stopic/3024">Interfaces and UX</a></li>
<li><a href="https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/stopic/3021">Reliability and Safety</a></li>
</ul>
<p>Continue reading <a href='https://www.oreilly.com/ideas/overcoming-barriers-to-ai-adoption'>Overcoming barriers to AI adoption.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/pHT1E91I6Ug" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/1raJGvb2OAQ" height="1" width="1" alt=""/>Ben Loricahttps://www.oreilly.com/ideas/overcoming-barriers-to-ai-adoptionhttp://feedproxy.google.com/~r/oreilly/radar/atom/~3/pHT1E91I6Ug/overcoming-barriers-to-ai-adoptionFour short links: 16 January 20192019-01-16T11:55:00Ztag:www.oreilly.com,2019-01-16:/ideas/four-short-links-16-january-2019<p><em>Compromised Hardware, Decision Tree Visualization, Calculus and Neural Networks, and Engineering Management</em></p><ol>
<li>
<a href="https://trmm.net/Modchips">Modchips</a> -- detailed talk exploring how plausible the Bloomberg-reported compromised hardware story is.</li>
<li>
<a href="https://github.com/parrt/dtreeviz">dtreeviz</a> -- <i>A python library for decision tree visualization and model interpretation.</i>
</li>
<li>
<a href="https://medium.com/mit-technology-review/a-radical-new-neural-network-design-could-overcome-big-challenges-in-ai-56b6af3fe9a5">Calculus and Neural Nets</a> (MIT TR) -- readable article about <a href="https://arxiv.org/abs/1806.07366">this paper</a>, which replaces layers in a neural network with calculus: <i>Calculus gives you all these nice equations for how to calculate a series of changes across infinitesimal steps—in other words, it saves you from the nightmare of modeling continuous change in discrete units.</i>
</li>
<li>
<a href="https://charity.wtf/2019/01/04/engineering-management-the-pendulum-or-the-ladder/">Engineering Management: The Pendulum or the Ladder</a> (Charity Majors) -- excellent advice to engineers faced with the choice or interest to go into engineering management.</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-16-january-2019'>Four short links: 16 January 2019.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/Lin11uDru0s" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/fl_s6aQ88rE" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-16-january-2019http://feedproxy.google.com/~r/oreilly/radar/atom/~3/Lin11uDru0s/four-short-links-16-january-2019Four short links: 15 January 20192019-01-15T12:00:00Ztag:www.oreilly.com,2019-01-15:/ideas/four-short-links-15-january-2019<p><em>Inside Actions, Live Coding, Science is Hard, and Censorship Factories</em></p><ol>
<li>
<a href="https://blog.jessfraz.com/post/the-life-of-a-github-action/">The Life of a GitHub Action</a> (Jessie Frazelle) -- <i>When you go through orientation at Google, they walk you through “The Life of a Query,” and it was one of my favorite things. So, I am re-applying the same for a GitHub Action.</i>
</li>
<li>
<a href="https://www.youtube.com/watch?v=oQIwe4i_pTo">Live Coding: OSCON Edition</a> (Suze Hinton) -- <i>an 8-minute live "speed run" of me coding JavaScript to remotely control an Arduino</i>. (via <a href="https://twitter.com/noopkat/status/1084944991972077569">Twitter</a>)</li>
<li>
<a href="https://www.nature.com/articles/s41562-018-0506-1">The Association between Adolescent Well-being and Digital Technology Use</a> (Nature) -- <i>The widespread use of digital technologies by young people has spurred speculation that their regular use negatively impacts psychological well-being. Current empirical evidence supporting this idea is largely based on secondary analyses of large-scale social data sets. Though these data sets provide a valuable resource for highly powered investigations, their many variables and observations are often explored with an analytical flexibility that marks small effects as statistically significant, thereby leading to potential false positives and conflicting results. Here we address these methodological challenges by applying specification curve analysis (SCA) across three large-scale social data sets (total n = 355,358) to rigorously examine correlational evidence for the effects of digital technology on adolescents. The association we find between digital technology use and adolescent well-being is negative but small, explaining at most 0.4% of the variation in well-being. Taking the broader context of the data into account suggests these effects are too small to warrant policy change.</i> As <a href="https://twitter.com/OrbenAmy/status/1084855999821959169">an author said on Twitter</a>, "The paper powerfully visualizes that without pre-registering analysis plans beforehand, analytical bias can allow researchers to tell almost any story with powerful data resources."</li>
<li>
<a href="https://www.nytimes.com/2019/01/02/business/china-internet-censor.html">China's Censorship Factories</a> (NY Times) -- someone has to learn what objectionable things are being said online so the filters can be tuned properly. <i>Beyondsoft employs over 4,000 workers like Mr. Li at its content reviewing factories. That is up from about 200 in 2016. They review and censor content day and night. [...] Many online media companies have their own internal content review teams, sometimes numbering in the thousands. They are exploring ways to get artificial intelligence to do the work. The head of the AI lab at a major online media company, who asked for anonymity because the subject is sensitive, said the company had 120 machine learning models. [...] New hires start with weeklong “theory” training, during which senior employees teach them the sensitive information they didn’t know before.</i> Honestly, I could quote the whole thing. It's a wtf paradise—like William Gibson and George Orwell got drunk and sketched a story.</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-15-january-2019'>Four short links: 15 January 2019.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/qn0Un46bR2s" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/cVDrTqd1Z54" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-15-january-2019http://feedproxy.google.com/~r/oreilly/radar/atom/~3/qn0Un46bR2s/four-short-links-15-january-20199 AI trends on our radar2019-01-15T11:00:00Ztag:www.oreilly.com,2019-01-15:/ideas/9-ai-trends-on-our-radar<p><img src='https://d3ucjech6zwjp8.cloudfront.net/600x450/ai-telescope-on-balcony-crop-9063c0e8adedb0d9c269a4951b6dfaeb.jpg'/></p><p><em>How new developments in automation, machine deception, hardware, and more will shape AI.</em></p><p>Here are key AI trends business leaders and practitioners should watch in the months ahead.</p>
<h2>We will start to see technologies enable partial automation of a variety of tasks.</h2>
<p>Automation occurs in stages. While full automation might still be a ways off, there are many workflows and tasks that lend themselves to partial automation. In fact, <a href="https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/four-fundamentals-of-workplace-automation">McKinsey estimates</a> that “fewer than 5% of occupations can be entirely automated using current technology. However, about 60% of occupations could have 30% or more of their constituent activities automated.”</p>
<p>We have already seen some interesting products and services that rely on computer vision and speech technologies, and we expect to see even more in 2019. Look for additional improvements in language models and robotics that will result in solutions that target text and physical tasks. Rather than waiting for a complete automation model, competition will drive organizations to implement partial automation solutions—and the success of those partial automation projects will spur further development.</p>
<h2>AI in the enterprise will build upon existing analytic applications.</h2>
<p>Companies have spent the last few years building processes and infrastructure to unlock disparate data sources in order to improve analytics on their most mission-critical analysis, whether it is business analytics, recommenders and personalization, forecasting, or anomaly detection and monitoring.</p>
<p>Aside from new systems that use vision and speech technologies, we expect early forays into deep learning and reinforcement learning will be in areas where companies already have data and machine learning in place. For example, companies are infusing their systems for temporal and geospatial data with deep learning, resulting in scalable and more accurate <em>hybrid </em>systems (i.e., systems that combine deep learning with other machine learning methods).</p>
<h2>In an age of partial automation and human-in-the-loop solutions, UX/UI design will be critical.</h2>
<p>Many current AI solutions work hand in hand with consumers, human workers, and domain experts. These systems improve the productivity of users and in many cases enable them to perform tasks at incredible scale and accuracy. Proper UX/UI design not only streamlines those tasks but also goes a long way toward getting users to trust and use AI solutions.</p>
<h2>We will see specialized hardware for sensing, model training, and model inference.</h2>
<p>The resurgence in deep learning began around 2011 with record-setting models in speech and computer vision. Today, there is certainly enough scale to justify specialized hardware—Facebook alone makes trillions of predictions per day. Google, too, has had enough scale to justify producing its own specialized hardware: it has been using its tensor processing units (TPUs) in its cloud since last year. 2019 should see a broader selection of specialized hardware begin to appear. Numerous companies and startups in China and the US have been working on hardware that targets model building and inference, both in the data center and on edge devices.</p>
<h2>AI solutions will continue to rely on hybrid models.</h2>
<p>While deep learning continues to drive a lot of interesting research, most end-to-end solutions <a href="https://www.oreilly.com/ideas/building-tools-for-the-ai-applications-of-tomorrow">are hybrid systems</a>. In 2019, we’ll begin to hear more about the essential role of other components and methods—including model-based methods like Bayesian inference, tree search, evolution, knowledge graphs, simulation platforms, and many more. And we just might begin to see exciting developments in machine learning methods that aren’t based on neural networks.</p>
<h2>AI successes will spur investments in new tools and processes.</h2>
<p>We are in a highly empirical era for machine learning. Tools for ML development will need to account for the importance of data, experimentation and model search, and model deployment and monitoring. Take just one step of the process: model building. Companies are beginning to look into tools for data lineage, metadata management and analysis, efficient utilization of compute resources, efficient model search, and hyperparameter tuning. In 2019, expect many new tools to ease the development and actual deployment of AI and Ml to products and services.</p>
<h2>Machine deception will remain a serious challenge.</h2>
<p>In spite of a barrage of “fake” news, we’re still in the early days of machine-generated content (fake images, video, audio, and text). At least for now, detection and forensic technologies have been able to ferret out fake video and images. But the tools for generating fake content are improving quickly, so <a href="https://www.darpa.mil/program/media-forensics">funding agencies in the US</a> and elsewhere have initiated programs to make sure detection technologies keep up.</p>
<p>And machine deception does not just refer to machines deceiving humans; machines deceiving machines (bots) and people deceiving machines (troll armies and click farms) can be just as difficult to deal with. Information propagation methods and click farms will continue to be used to fool ranking systems on content and retail platforms, and methods to detect and combat this will have to be developed as fast as new forms of machine deception are launched.</p>
<h2>Reliability and safety will take center stage.</h2>
<p>It’s been heartening to see researchers and practitioners become seriously interested and engaged in issues pertaining to <a href="https://www.oreilly.com/ideas/data-collection-and-data-markets-in-the-age-of-privacy-and-machine-learning?twitter=@bigdata">privacy, fairness, and ethics</a>. But as AI systems become deployed in mission-critical applications—and even life and death scenarios involving applications such as autonomous vehicles or healthcare—improved efficiency from automation will need to come with safety and reliability measurements and guarantees. The rise of machine deception in online platforms, as well as recent accidents involving autonomous vehicles, has cracked this issue wide open. In 2019, expect to hear safety discussed more intensively.</p>
<h2>Democratizing access to large training data will level the playing field.</h2>
<p>Because many of the models we rely on—including deep learning and reinforcement learning— are data hungry, the anticipated winners in the field of AI have been huge companies or countries with access to massive amounts of data. But services for generating labeled datasets (specifically companies that rely on human labelers) are beginning to use machine learning tools to help their human workers scale and improve their accuracy. And in certain domains, new tools like generative adversarial networks (GAN) and simulation platforms are able to provide realistic synthetic data, which can be used to train machine learning models. Finally, a new crop of secure and privacy-preserving technologies that facilitate sharing of data across organizations are helping companies take advantage of data they didn’t generate. Together, these developments will help smaller organizations compete using machine learning and AI.</p>
<p>Continue reading <a href='https://www.oreilly.com/ideas/9-ai-trends-on-our-radar'>9 AI trends on our radar.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/OMlYUCpJ-9s" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/PXgag2FyYtU" height="1" width="1" alt=""/>Ben Loricahttps://www.oreilly.com/ideas/9-ai-trends-on-our-radarhttp://feedproxy.google.com/~r/oreilly/radar/atom/~3/OMlYUCpJ-9s/9-ai-trends-on-our-radarFour short links: 14 January 20192019-01-14T12:50:00Ztag:www.oreilly.com,2019-01-14:/ideas/four-short-links-14-january-2019<p><em>Software Patents, Learning Artistic Styles, Decentralized Commerce, and Open Source Notes Software</em></p><ol>
<li>
<a href="https://boingboing.net/2019/01/10/federal-troll-circuit.html">Software Patents Slipping Back</a> (BoingBoing) -- USPTO issuing new guidance that re-enables crappy software patenting.</li>
<li>
<a href="https://hal.inria.fr/hal-01802131v2/document">Unsupervised Learning of Artistic Styles with Archetypal Style Analysis</a> -- <i>Our objective is to automatically discover, summarize, and manipulate artistic styles present in the collection.</i> (via <a href="https://blog.acolyer.org/2019/01/11/unsupervised-learning-of-artistic-styles-with-archetypal-style-analysis/">Adrian Colyer</a>)</li>
<li>
<a href="https://opaque.link/post/dropgang/">Dropgangs, or the Future of Darknet Markets</a> -- <i>The other major change is the use of “dead drops” instead of the postal system, which has proven vulnerable to tracking and interception. Now, goods are hidden in publicly accessible places like parks, and the location is given to the customer on purchase. The customer then goes to the location and picks up the goods.</i>
</li>
<li>
<a href="https://github.com/thenativeweb/wolkenkit-boards">Boards</a> -- open source tool for collaboratively organizing notes.</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-14-january-2019'>Four short links: 14 January 2019.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/bF-kKokjN3A" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/-zfxqTCCTM0" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-14-january-2019http://feedproxy.google.com/~r/oreilly/radar/atom/~3/bF-kKokjN3A/four-short-links-14-january-2019Four short links: 11 January 20192019-01-11T21:00:00Ztag:www.oreilly.com,2019-01-11:/ideas/four-short-links-11-january-2019<p><em>Storage Orchestration, Trolls and Media, Language Bias, and AI Attitudes</em></p><ol>
<li>
<a href="https://github.com/rook/rook">Rook</a> -- <i>storage orchestration for Kubernetes.</i>
</li>
<li>
<a href="https://mitpress.mit.edu/books/why-we-cant-have-nice-things">Why We Can't Have Nice Things</a> (MIT Press) -- <i>Trolls' actions are born of and fueled by culturally sanctioned impulses—which are just as damaging as the trolls' most disruptive behaviors. [...] For trolls, exploitation is a leisure activity; for media, it's a business strategy.</i> (via <a href="https://twitter.com/gr3gjsmith/status/1083024752682373121">Greg J. Smith</a>)</li>
<li>
<a href="http://lup.lub.lu.se/luur/download?func=downloadFile&amp;recordOId=2971193&amp;fileOId=2971195">Language Bias in Accident Investigation</a> -- <i>The SAIG [Forest Service's Serious Accident Investigation Guide] influences investigators to apply linear, hindsight-biased, "cause and effect" reasoning toward human actors in the event. The guide’s use of agentive descriptions, binary opposition, and the active verb voice creates a seemingly exclusive causal attribution toward humans. Objective analysis was found to be impossible, using the SAIG's language and report structure. This stands in contrast to the agency's goal of accident prevention</i>. <em>nota bene</em>, post-mortem facilitators. (via <a href="https://twitter.com/allspaw/status/1083178123900915714">John Allspaw</a>)</li>
<li>
<a href="https://governanceai.github.io/US-Public-Opinion-Report-Jan-2019/">Artificial Intelligence: American Attitudes and Trends</a> -- <i>This report is based on findings from a nationally representative survey conducted by the Center for the Governance of AI, housed at the Future of Humanity Institute, University of Oxford, using the survey firm YouGov. The survey was conducted between June 6 and 14, 2018, with a total of 2,000 American adults (18+) completing the survey.</i> Findings include <i>Demographic characteristics account for substantial variation in support for developing high-level machine intelligence. There is substantially more support for developing high-level machine intelligence by those with larger reported household incomes, such as those earning over $100,000 annually (47%) than those earning less than $30,000 (24%); by those with computer science or programming experience (45%) than those without (23%); by men (39%) than women (25%). These differences are not easily explained away by other characteristics (they are robust to our multiple regression).</i> (via <a href="https://twitter.com/Miles_Brundage/status/1083023712985714690">Miles Brundage</a>)</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-11-january-2019'>Four short links: 11 January 2019.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/nIo6LPBrV4I" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/c2CjyDnG8kI" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-11-january-2019http://feedproxy.google.com/~r/oreilly/radar/atom/~3/nIo6LPBrV4I/four-short-links-11-january-2019Four short links: 10 January 20192019-01-10T13:15:00Ztag:www.oreilly.com,2019-01-10:/ideas/four-short-links-10-january-2019<p><em>Post Mortems, GDPR Implementation, Feature Engineering, and State of Security</em></p><ol>
<li>
<a href="https://github.com/danluu/post-mortems">Post Mortems</a> (Dan Luu) -- a collection of outage postmortems from big and small companies. (via <a href="https://twitter.com/lvanbever/status/1082534240401739777">Laurent Vanbever</a>)</li>
<li>
<a href="https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-the-general-data-protection-regulation-gdpr/">Guide to GDPR</a> -- UK's guide. <i>It explains each of the data protection principles, rights, and obligations. It summarizes the key points you need to know, answers frequently asked questions, and contains practical checklists to help you comply.</i>
</li>
<li>
<a href="https://github.com/Featuretools/featuretools">Featuretools</a> -- <i>open source Python framework for automated feature engineering.</i>
</li>
<li>
<a href="https://noncombatant.org/2019/01/06/state-of-security-2019/">The State of Security in 2019</a> -- <i>The high-order bit in much of the below is complexity. Hardware, software, platforms, and ecosystems are often way too complex, and a whole lot of our security, privacy, and abuse problems stem from that.</i> Lots of really good links and ideas here.</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-10-january-2019'>Four short links: 10 January 2019.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/x93gS-fC3MM" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/ihqUgEHD8fY" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-10-january-2019http://feedproxy.google.com/~r/oreilly/radar/atom/~3/x93gS-fC3MM/four-short-links-10-january-2019Gradually, then suddenly2019-01-10T11:00:00Ztag:www.oreilly.com,2019-01-10:/ideas/gradually-then-suddenly<p><img src='https://d3ucjech6zwjp8.cloudfront.net/600x450/economy-sunrise-crop-ea377cdf383009c452c2678cba3912bb.jpg'/></p><p><em>Technological change often happens gradually, then suddenly. Tim O'Reilly explores the areas poised for sudden shifts. </em></p><p>There’s a passage in Ernest Hemingway’s novel <em>The Sun Also Rises</em> in which a character named Mike is asked how he went bankrupt. “Two ways,” he answers. “Gradually, then suddenly.”</p>
<p>Technological change happens much the same way. Small changes accumulate, and suddenly the world is a different place. Throughout my career at O’Reilly Media, we’ve tracked and fostered a lot of “gradually, then suddenly” movements: the World Wide Web, open source software, big data, cloud computing, sensors and ubiquitous computing, and now the pervasive effects of AI and algorithmic systems on society and the economy.</p>
<p>What are some of the things that are in the middle of their “gradually, then suddenly” transition right now? The list is long; here are a few of the areas that are on my mind.</p>
<h2>AI and algorithms are everywhere</h2>
<p>The most important trend for readers to focus on is the development of new kinds of partnership between human and machine. We take for granted that algorithmic systems do much of the work at online sites like Google, Facebook, Amazon, and Twitter, but we haven’t fully grasped the implications. These systems are hybrids of human and machine. Uber, Lyft, and Amazon Robotics brought this pattern to the physical world, reframing the corporation as a vast, buzzing network of humans both guiding and guided by machines. In these systems, the algorithms decide who gets what and why; they’re changing the fundamentals of market coordination in ways that gradually, then suddenly, will become apparent.</p>
<h2>The rest of the world is leapfrogging the US</h2>
<p>The volume of mobile payments in China is <a href="https://mobilepaymentconference.com/why-china-leads-the-world-in-mobile-payments/">$13 trillion versus the US’ $50 billion</a>, while credit cards never took hold. Already <a href="https://www.wired.com/story/wired25-anne-wojcicki-keller-rinaudo-zipline-medical-drones/">Zipline’s on-demand drones are delivering 20% of all blood supplies in Rwanda</a> and will be coming soon to other countries (including the US). In each case, the lack of existing infrastructure turned out to be an advantage in adopting a radically new model. Expect to see this pattern recur, as incumbents and old thinking hold back the adoption of new models.</p>
<h2>China and the transformation of Africa</h2>
<p>Speaking of Africa, if it isn’t on your radar, it should be. Gradually, then suddenly, it's becoming <em>“</em>the next factory of the world.” That’s the title of <a href="https://www.amazon.com/Next-Factory-World-Investment-Reshaping-ebook/dp/B01MUGYCME">a 2017 book by McKinsey’s Irene Sun</a>. There’s also a detailed McKinsey report, <em><a href="https://www.mckinsey.com/featured-insights/middle-east-and-africa/the-closest-look-yet-at-chinese-economic-engagement-in-africa">Dance of the Lions and Dragons</a></em>, based on a study of more than 1,000 Chinese-owned factories in Africa. As the US has withdrawn into a kind of neo-isolationism, China is stepping up. There's a lot of <a href="https://www.weforum.org/agenda/2018/09/three-myths-about-chinas-investment-in-africa-and-why-they-need-to-be-dispelled/">misinformation, rooted in denial, about its “One Belt, One Road” initiative</a>. Expect to wake up one day and realize that China has done to the US what the US did to the UK in the 20th century, becoming the new leader of the world economy, for good or ill. Up until now, China has spent a lot more time copying us than we spend copying them; that’s suddenly going to go into reverse. For a detailed look at the competition between the two “AI superpowers,” read <a href="https://www.amazon.com/AI-Superpowers-China-Silicon-Valley/dp/132854639X">Kai-Fu Lee’s book</a> of that name. See trend 1.</p>
<h2>The next agricultural revolution</h2>
<p>Last year, when I spoke at the <a href="https://foodtechconnect.com/events/">Food+Tech Connect Conference</a> in Amsterdam, I got an eyeful of the agricultural revolution that is happening in the Netherlands. Did you know that this tiny country, 1/270th the size of the US, is <a href="https://www.nationalgeographic.com/magazine/2017/09/holland-agriculture-sustainable-farming/">the world’s second-largest food exporter</a>? That’s a testament to the way that precision farming and other new technologies are transforming agriculture. Silicon Valley is waking up to the opportunity, and so are consumers. I stopped in at an Oakland sports bar recently, and what did I see on the menu but an <a href="https://impossiblefoods.com/food/">Impossible Burger</a>. This new meatless meat is no longer just a treat for tech elites. Expect meaningful change in the makeup of our food supply, what we consume, and how it gets to us. If you’re skeptical, remember that 25 years ago, the internet was just becoming mainstream, and even the smartphone revolution is only 10 years old. Gradually, then suddenly, both have transformed the world.</p>
<h2>Climate change</h2>
<p>You have to have huge ideological blinders on not to see that the effects of climate change are less and less “gradual” and that we are rushing headlong toward a “suddenly” moment. One of the most interesting discoveries for me in the past year has been the work of groups like <a href="https://sohp.fas.harvard.edu">the Initiative for the Science of the Human Past at Harvard</a>, which have been looking at the connection between climate change events and the fall of ancient civilizations. My friend <a href="http://www.malcolmwiener.net">Malcolm Wiener</a> pointed out to me that climate events trigger mass migrations, which often bring with them new plagues, and whether a civilization survives (<a href="https://press.princeton.edu/titles/11079.html">as the Roman Empire did, albeit on a reduced scale</a>) or falls depends on the quality of its ruling elites. I leave you to consider the implications of the current political moment.</p>
<h2>Genetic engineering</h2>
<p>Genetic engineering is an important driver of food innovation, but it’s also a huge part of the possible response to climate change. <a href="https://reviverestore.org/projects/woolly-mammoth/">Bring the wooly mammoth back to life</a>? <a href="https://med.stanford.edu/news/all-news/2018/04/crispr-used-to-genetically-edit-coral.html">Save coral reefs</a>? But climate adaptation is just the tip of the iceberg. Could we <a href="https://www.fastcompany.com/90257662/these-gorgeous-colors-come-from-dye-made-by-bacteria-not-chemicals">replace chemical dyes with bacterial by-products</a>? And don’t get me started on the application of genomics to healthcare. Back in 2010, George Church pointed out the equivalent of <a href="https://www.technologyreview.com/s/417628/a-moores-law-for-genetics/">Moore’s law for gene sequencing</a>. As a result of that acceleration, we’re now approaching the “suddenly” moment for precision medicine. And of course, AI is in the middle of all that, helping with drug discovery, synthesis of new materials, and biological pathways. But I suspect that there's also a hidden intersection with ...</p>
<h2>Neural interfaces</h2>
<p>One of my biggest “Wow!” moments of 2018 took place in the offices of <a href="https://www.youtube.com/watch?v=5Z5aZK2C3ew">neural interface company CTRL-labs</a>. Their demo involves someone playing the old <em>Asteroids</em> computer game without touching a keyboard, using machine learning to interpret the nerve signals that are sent to the hands. But it isn’t quite what you think. Moving things in the digital realm without moving your hands seems startling enough (though it’s worth remembering that it was once considered remarkable to be able to read silently without moving your lips). But that’s just the first stage. Essentially, users of this technology “grow” another virtual hand, which they can move independently of their physical hands. One of the researchers bowled me over when he said he was “working on controlling nine cursors at once.” Gradually, then suddenly, our children will interface with machines in deeper and deeper ways. Humanity is already going cyborg (see trend 1); expect it to accelerate. Don’t fall into the trap of thinking that AI will replace humans when it can be used even more powerfully to augment them.</p>
<h2>Online learning</h2>
<p>Online learning isn’t just about online schools like Udacity and Coursera or <a href="https://www.oreilly.com/online-learning/index.html">O’Reilly’s own learning platform</a>. What’s too often overlooked is how education and cognitive augmentation go hand in hand. The reason Uber and Lyft have a seemingly unlimited supply of drivers is because no training is required; the app itself does the heavy lifting of telling the driver where to pick up the passenger and how to get to the destination. At O’Reilly, we call this “<a href="https://trainingindustry.com/articles/content-development/the-next-big-shift-in-enterprise-learning-performance-adjacent-learning">performance-adjacent learning</a>.” Josh Bersin calls it “<a href="https://joshbersin.com/2018/06/a-new-paradigm-for-corporate-training-learning-in-the-flow-of-work/">learning in the flow of work</a>.” So many of the attempts to create online education seem to be reproducing 20th century models online; instead, we’ve hitched our platform squarely to the “gradually, then suddenly” trend of knowledge on demand, understanding that the supporting role of coursework is to get you to the point where you can take in and use on-demand knowledge. (We call this “structural literacy.”) See trend 1.</p>
<h2>The crisis of faith in government</h2>
<p>Ever since Jennifer Pahlka and I began working on the Gov 2.0 Summit back in 2008, we’ve been concerned that if we can’t get government up to speed on 21st century technology, a critical pillar of the good society will crumble. When we started that effort, we were focused primarily on government innovation; over time, through Jen’s work at <a href="https://www.codeforamerica.org/">Code for America</a> and the <a href="https://www.usds.gov/">United States Digital Service</a>, that shifted to a focus on making sure that government services actually work for those who need them most. Michael Lewis' latest book, <em><a href="https://www.amazon.com/Fifth-Risk-Michael-Lewis/dp/1324002646">The Fifth Risk</a></em>, highlights just how bad things might get if we continue to neglect and undermine the machinery of government. It’s not just the political fracturing of our country that should concern us; it’s the fact that government plays a critical role in infrastructure, in innovation, and in the safety net. That role has gradually been eroded, and the cracks that are appearing in the foundation of our society are coming at the worst possible time.</p>
<h2>Deeper reading</h2>
<p>Economics is my learning frontier right now as I explore the connections between the business ecosystems of the great tech platforms and trends in what I’ve been calling the Next Economy. Some of the books that I’ve taken the most from this year include <em><a href="https://www.amazon.com/Doughnut-Economics-Seven-21st-Century-Economist/dp/1603586741">Doughnut Economics</a></em>, by Kate Raworth; <em><a href="https://www.amazon.com/Value-Everything-Making-Taking-Economy/dp/161039674X">The Value of Everything</a></em>, by Mariana Mazzucato; <em><a href="https://www.amazon.com/How-Asia-Works-Joe-Studwell/dp/0802121322">How Asia Works</a></em>, by Joe Studwell; <em><a href="https://www.amazon.com/Assumptions-Economists-Make-Jonathan-Schlefer/dp/0674052269">The Assumptions Economists Make</a></em>, by Jonathan Schlefer; <em><a href="https://www.amazon.com/Prediction-Machines-Economics-Artificial-Intelligence/dp/1633695670">Prediction Machines</a></em>, by Ajay Agrawal, Joshua Gans, and Avi Goldfarb; and <em><a href="https://www.amazon.com/Adam-Smith-Change-Your-Life/dp/1591847958">How Adam Smith Can Change Your Life</a></em>, by Russ Roberts. That final book is not at all what most people will expect from the title. It's not about the “invisible hand” or <em>The Wealth of Nations</em> but about Adam Smith’s other great book, <em>The Theory of Moral Sentiments</em>, which explores the role of social norms as a check on self-interest. We must rediscover and reinvent those norms, or gradually, then suddenly, we'll continue the descent into economic and political barbarism.</p>
<p>Rather than ending this newsletter on a down note, let me remind you that the future is not inevitable. As I wrote in <a href="https://oreil.ly/2Hgrnoo">my book</a> last year, it's up to us:</p>
<blockquote>
<p>This is my faith in humanity: that we can rise to great challenges. Moral choice, not intelligence or creativity, is our greatest asset. Things may get much worse before they get better. But we can choose instead to lift each other up, to build an economy where people matter, not just profit. We can dream big dreams and solve big problems. Instead of using technology to replace people, we can use it to augment them so they can do things that were previously impossible.</p>
</blockquote>
<p>Continue reading <a href='https://www.oreilly.com/ideas/gradually-then-suddenly'>Gradually, then suddenly.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/HiB6JL2AGF4" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/xdCtUYQKuB4" height="1" width="1" alt=""/>Tim O'Reillyhttps://www.oreilly.com/ideas/gradually-then-suddenlyhttp://feedproxy.google.com/~r/oreilly/radar/atom/~3/HiB6JL2AGF4/gradually-then-suddenlyFour short links: 9 January 20192019-01-09T12:00:00Ztag:www.oreilly.com,2019-01-09:/ideas/four-short-links-9-january-2019<p><em>Quantum Computing Zines, Phone Locations, Secret Wi-Fi Networks, and Programming the Integers</em></p><ol>
<li>
<a href="https://www.epiqc.cs.uchicago.edu/zines">Quantum Computing Zines</a> -- from <a href="https://www.epiqc.cs.uchicago.edu/">EPiQC</a>, the University of Chicago-led quantum research collaboration. Topics: history, hype, measurement, operations, notation, reversibility, superposition, and entanglement.</li>
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<a href="https://motherboard.vice.com/amp/en_us/article/nepxbz/i-gave-a-bounty-hunter-300-dollars-located-phone-microbilt-zumigo-tmobile">Surprising People Have Access to Your Phone's Location</a> (VICE) -- <i>T-Mobile, Sprint, and AT&amp;T are selling access to their customers’ location data, and that data is ending up in the hands of bounty hunters and others not authorized to possess it, letting them track most phones in the country.</i>
</li>
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<a href="https://hackaday.com/2019/01/04/underclocking-the-esp8266-leads-to-wifi-weirdness/">Underclocking the ESP8266 Leads to Wi-Fi Weirdness</a> (Hackaday) -- underclock an 8266 and the channel width decreases proportionally. Underclock two by the same amount and you can create a channel so narrow that non-underclocked devices can't understand it. Clever!</li>
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<a href="https://www.scottaaronson.com/incompleteness.pdf">Gödel Was Incompleteness Ex Machina</a> -- <i>In this essay we’ll prove Gödel’s incompleteness theorems twice. First, we’ll prove them the good old-fashioned way. Then we’ll repeat the feat in the setting of computation. In the process, we’ll discover that Gödel’s work, rightly viewed, needs to be split into two parts: the transport of computation into the arena of arithmetic on the one hand and the actual incompleteness theorems on the other. After we’re done, there will be cake.</i> (via <a href="https://twitter.com/daniel_bilar/status/1080993342308188162">Daniel Bilar</a>)</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-9-january-2019'>Four short links: 9 January 2019.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/t_gxbMy7HKE" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/QKPk3oFZl-o" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-9-january-2019http://feedproxy.google.com/~r/oreilly/radar/atom/~3/t_gxbMy7HKE/four-short-links-9-january-2019Four short links: 8 January 20192019-01-08T11:55:00Ztag:www.oreilly.com,2019-01-08:/ideas/four-short-links-8-january-2019<p><em>Visual Attention, Git Server, Cryptocurrency Security, and Strategy vs. Tactics</em></p><ol>
<li>
<a href="https://www.pnas.org/content/116/1/328.long">Implicit Model of Other People’s Visual Attention as an Invisible, Force-Carrying Beam Projecting from the Eyes</a> -- I wonder how that affects VR/AR interaction design. <i>Here we report that people automatically and unconsciously treat other people’s eyes as if beams of force-carrying energy emanate from them, gently pushing on objects in the world.</i>
</li>
<li>
<a href="https://onedev.io/">OneDev</a> -- <i>The opinionated but practical self-hosted git server</i>. Interesting set of pro features for power users. The product manager in me always says, "cool, but how do you compete with GitHub and GitLab? Any useful features can be copied by their armies of developers. Features are not defensible." Good luck to 'em, though. (And if this is open source, they don't need to "compete" in a classic way; winning can be whatever the developers want it to be.)</li>
<li>
<a href="https://twitter.com/etherchain_org/status/1082329360948969472">Successful 51% Attack on Ethereum Classic</a> -- though, as <a href="https://twitter.com/sminnee/status/1082464598698057728">Sam Minnée said on Twitter</a>, "Ethereum Classic is the Windows XP of Ethereum." This as <a href="https://marginalrevolution.com/marginalrevolution/2019/01/bitcoin-much-less-secure-people-think.html">Bitcoin is less secure than most people think</a>: <i>As an example, Budish shows that if the attacker has just 5% more computational power than the honest nodes, then on average it takes 26.5 blocks (a little over four hours) for the attacker to have the longest chain. (Most of the time it takes far fewer blocks, but occasionally it takes hundreds of blocks for the attacker to produce the longest chain.) The attack will always be successful eventually; the key question is what is the cost of the attack?</i>
</li>
<li>
<a href="https://diogomonica.com/2018/10/07/a-pirates-take-on-strategy-vs-tactics/">Pirate's Take on Strategy vs. Tactics</a> -- useful to give to That Person on your team who misuses the words.</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-8-january-2019'>Four short links: 8 January 2019.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/D2mhhf8cMjk" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/sPK_Ibi44bk" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-8-january-2019http://feedproxy.google.com/~r/oreilly/radar/atom/~3/D2mhhf8cMjk/four-short-links-8-january-20197 data trends on our radar2019-01-08T11:00:00Ztag:www.oreilly.com,2019-01-08:/ideas/7-data-trends-on-our-radar<p><img src='https://d3ucjech6zwjp8.cloudfront.net/600x450/data-telescope-crop-2e3aad0d928a04a4ca3b8f625a4fac3a.jpg'/></p><p><em>From infrastructure to tools to training, Ben Lorica looks at what’s ahead for data.</em></p><p>Whether you’re a business leader or a practitioner, here are key data trends to watch and explore in the months ahead.</p>
<h2>Increasing focus on building data culture, organization, and training</h2>
<p>In <a href="https://www.oreilly.com/data/free/how-companies-are-putting-aI-to-work-through-deep-learning.csp">a recent O’Reilly survey</a>, we found that the skills gap remains one of the key challenges holding back the adoption of machine learning. The demand for data skills (“the sexiest job of the 21st century”) hasn’t dissipated. <a href="https://economicgraph.linkedin.com/resources/linkedin-workforce-report-august-2018">LinkedIn recently found</a> that demand for data scientists in the US is “off the charts,” and our survey indicated that the demand for data scientists and data engineers is strong not just in the US but globally.</p>
<p>With the average shelf life of a skill today at less than five years and <a href="https://www.huffingtonpost.com/julie-kantor/high-turnover-costs-way-more-than-you-think_b_9197238.html">the cost to replace an employee</a> estimated at between six and nine months of the position’s salary, there is increasing pressure on tech leaders to retain and upskill rather than replace their employees in order to keep data projects (such as machine learning implementations) on track. We are also seeing more <a href="https://conferences.oreilly.com/strata/strata-ca/public/schedule/topic/2867">training programs aimed at executives and decision makers</a>, who need to understand how these new ML technologies can impact their current operations and products.</p>
<p>Beyond investments in narrowing the skills gap, companies are beginning to put processes in place for their data science projects, for example <a href="https://www.oreilly.com/ideas/transforming-organizations-through-analytics-centers-of-excellence">creating analytics centers of excellence</a> that centralize capabilities and share best practices. Some companies are also actively maintaining a portfolio of use cases and opportunities for ML.</p>
<h2>Cloud for data infrastructure</h2>
<p>Cloud platforms will continue to draw companies that need to invest in data infrastructure: not only do the cloud platforms have improving foundational technologies and managed services, but increasingly software vendors and popular open source data projects are making sure their offerings are easy to run in the cloud. According to a recent O’Reilly survey, 85% of respondents said they already had some of their data infrastructure in the cloud, and other surveys of IT executives reveal that many are planning to increase their investments in SaaS and cloud tools. Data engineers and data scientists are beginning to use new cloud technologies, like serverless, for some of their tasks.</p>
<h2>Continuing investments in (emerging) data technologies</h2>
<p>For most companies, the road toward machine learning (ML) involves simpler analytic applications. This is good news because ML demands data, and many of the simpler analytic tools that precede ML already require data infrastructure to be in place. The growing interest in ML will spur companies to continue to invest in the foundational data technologies that are required to scale ML initiatives. This includes items like data ingestion and integration, storage and data processing, and data preparation and cleaning.</p>
<h2>Tools for secure and privacy-preserving analytics</h2>
<p>Companies will continue to invest in tools for data security and privacy, but we expect to see an increased focus on tools for privacy-preserving analytics—areas where researchers and startups have been actively engaged. Organizations will begin to identify and manage risks that accompany the use of machine learning in products and services, such as security and privacy, bias, safety, and lack of transparency.</p>
<h2>Sustaining machine learning in an enterprise</h2>
<p>Early indications are that many organizations are correctly focusing their initial machine learning projects (and investments) in use cases that improve their most mission-critical analysis projects. For example, <a href="https://www.safaribooksonline.com/videos/the-artificial-intelligence/9781492025894/9781492025894-video323403">financial service companies are investing ML in risk analysis, telecom companies are applying AI to service operations, and automotive companies are focusing their initial ML implementations in manufacturing</a>. This is also reflected by the emergence of tools that are specific to machine learning, including data science platforms, data lineage, metadata management and analysis, data governance, and model lifecycle management.</p>
<h2>Burgeoning IoT technologies</h2>
<p>A few years ago, most internet of things (IoT) examples involved smart cities and smart governments. But the rise of cloud platforms, cheap sensors, and machine learning has IoT poised to make a comeback in industry. We’ll still hear about municipal and public sector applications, but there are other interesting use cases involving closed systems (factories, buildings, homes) and enterprise and consumer applications (edge computing).</p>
<h2>Automation in data science and data</h2>
<p>As the use of machine learning and analytics becomes more widespread, we need tools that will allow data scientists and data engineers to scale so they can tackle many more problems and maintain more systems. This will lead to more automation tools for the many stages involved in data science, including data preparation, feature engineering, model selection, and hyperparameter tuning, as well as data engineering and data operations. There are already some early <a href="https://www.oreilly.com/ideas/what-machine-learning-means-for-software-development">applications of machine learning</a> aimed at the partial automation of tasks in data science, software development, and IT operations.</p>
<p>Continue reading <a href='https://www.oreilly.com/ideas/7-data-trends-on-our-radar'>7 data trends on our radar.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/NqVxbGxCBxk" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/aooOhcddBA8" height="1" width="1" alt=""/>Ben Loricahttps://www.oreilly.com/ideas/7-data-trends-on-our-radarhttp://feedproxy.google.com/~r/oreilly/radar/atom/~3/NqVxbGxCBxk/7-data-trends-on-our-radarFour short links: 7 January 20192019-01-07T12:00:00Ztag:www.oreilly.com,2019-01-07:/ideas/four-short-links-7-january-2019<p><em>Named Tensors, Project Management Aphorisms, Quantum Roadmap, and Deep Learning</em></p><ol>
<li>
<a href="http://nlp.seas.harvard.edu/NamedTensor">Tensor Considered Harmful</a> -- <i>Trap 1: Privacy by Convention; Trap 2: Broadcasting by Alignment; Trap 3: Access by Comments.</i> Author proposes a named tensor to tackle these problems. (via <a href="https://twitter.com/daniel_bilar/status/1081240400621457408">Daniel Bilar</a>)</li>
<li>
<a href="https://llis.nasa.gov/lesson/1956">100 Lessons Learned for Project Managers</a> (NASA) -- <i>This material first appeared in the October 2003 issue of NASA's ASK Magazine, which now lists 122 of these aphorisms.</i> Examples: <i>People who monitor work and don't help get it done, never seem to know exactly what is going on. Integrity means your subordinates trust you. An agency's age can be estimated by the number of reports and meetings it has. The older it gets, the more the paperwork increases and the less product is delivered per dollar. Many people have suggested that an agency self-destruct every 25 years and be reborn starting from scratch.</i>
</li>
<li>
<a href="https://www.technologyreview.com/s/612596/the-man-turning-china-into-a-quantum-superpower/">The Man Turning China into a Quantum Superpower</a> (MIT TR) -- <i>One of the reasons China has done so well in quantum science is the close coordination between its government research groups, the Chinese Academy of Sciences, and the country’s universities. Europe now has its own quantum master plan to prompt such collaborations, but the U.S. has been slow to produce a comprehensive strategy for developing the technologies and building a future quantum workforce.</i> Where's quantum's Licklider?</li>
<li>
<a href="http://d2l.ai/chapter_introduction/index.html">Dive Into Deep Learning</a> -- Berkeley University course. Uses Jupyter Notebooks and MXNet (not TensorFlow or PyTorch).</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-7-january-2019'>Four short links: 7 January 2019.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/k2eRg0-5pPo" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/f_JhNXUJufo" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-7-january-2019http://feedproxy.google.com/~r/oreilly/radar/atom/~3/k2eRg0-5pPo/four-short-links-7-january-2019Four short links: 4 January 20192019-01-04T11:15:00Ztag:www.oreilly.com,2019-01-04:/ideas/four-short-links-4-january-2019<p><em>State of the World, NLP Toolkit, Fair AI, and Upgrade Your Soldering Iron</em></p><ol>
<li>
<a href="https://people.well.com/conf/inkwell.vue/topics/506/State-of-the-World-2019-page01.html">Bruce Sterling's State of the World</a> -- this year's guest, James Bridle. <i>It's quite clear that many things being currently constructed, from large-scale capitalist enterprises to social media timelines to microinteractions on smartphone apps, are specifically designed as attacks on our ability to think clearly and act autonomously: "the race to the bottom of the brain stem," as Tristan Harris puts it. What you're feeling is not some weird emergent effect of too much screen time: it's deliberate.</i> (via <a href="https://boingboing.net/2019/01/03/global-ukraine.html">BoingBoing</a>)</li>
<li>
<a href="https://github.com/zalandoresearch/flair">Flair</a> -- <i>very simple framework for state-of-the-art NLP</i>. Multilingual, built on PyTorch.</li>
<li>
<a href="https://www.itu.int/en/journal/002/Documents/ITU2018-15.pdf">Towards a Human Artificial Intelligence for Human Development</a> -- Sandy Pentland was a co-author, so it caught my eye. <i>This paper discusses the possibility of applying the key principles and tools of current artificial intelligence (AI) to design future human systems in ways that could make them more efficient, fair, responsive, and inclusive</i>.</li>
<li>
<a href="https://github.com/Ralim/ts100">TS100</a> -- new open source firmware for your soldering iron. You had me at "soldering iron with flashable firmware"...</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-4-january-2019'>Four short links: 4 January 2019.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/AtwRaUaR5qE" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/aDB7O6qmK8E" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-4-january-2019http://feedproxy.google.com/~r/oreilly/radar/atom/~3/AtwRaUaR5qE/four-short-links-4-january-2019In the age of AI, fundamental value resides in data2019-01-03T11:30:00Ztag:www.oreilly.com,2019-01-03:/ideas/in-the-age-of-ai-fundamental-value-resides-in-data<p><img src='https://d3ucjech6zwjp8.cloudfront.net/600x450/beijing_cbd_2015_september_night_crop-42efe66eae02f620af25fa0184e62294.jpg'/></p><p><em>The O’Reilly Data Show Podcast: Haoyuan Li on accelerating analytic workloads, and innovation in data and AI in China.</em></p><p>In this episode of the <a href="https://www.oreilly.com/ideas/topics/oreilly-data-show-podcast">Data Show</a>, I spoke with Haoyuan Li, CEO and founder of <a href="https://www.alluxio.com/">Alluxio</a>, a startup commercializing <a href="https://www.alluxio.org/">the open source project with the same name</a> (full disclosure: I’m an advisor to Alluxio). Our discussion focuses on the state of Alluxio (the open source project that has <a href="https://www.oreilly.com/ideas/tachyon-open-source-distributed-fault-tolerant-in-memory-file-system">roots in UC Berkeley’s AMPLab</a>), specifically emerging use cases here and in China. Given the large-scale use in China, I also wanted to get Li’s take on the state of data and AI technologies in Beijing and other parts of China.</p><p>Continue reading <a href='https://www.oreilly.com/ideas/in-the-age-of-ai-fundamental-value-resides-in-data'>In the age of AI, fundamental value resides in data.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/HQllCXrV5Tg" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/YWmCcMk9Ggk" height="1" width="1" alt=""/>Ben Loricahttps://www.oreilly.com/ideas/in-the-age-of-ai-fundamental-value-resides-in-datahttp://feedproxy.google.com/~r/oreilly/radar/atom/~3/HQllCXrV5Tg/in-the-age-of-ai-fundamental-value-resides-in-dataFour short links: 3 January 20192019-01-03T11:00:00Ztag:www.oreilly.com,2019-01-03:/ideas/four-short-links-3-january-2019<p><em>Raw Data, Learning Text Adventures, Algorithms Textbook, and Physical Computing</em></p><ol>
<li>
<a href="https://www.thenewatlantis.com/publications/why-data-is-never-raw">Why Data is Never Raw</a> -- <i>In scientific research, the choice of what to measure and how is fundamental. But in many cases, especially in the social sciences, what we want to capture doesn’t already have a clear measurement. It must therefore be “operationalized” somehow—meaning we must create a technique for measuring it. This necessarily requires emphasizing some aspects over others. Just as thought involves focusing, data collection involves narrowing attention; something is always left out.</i>
</li>
<li>
<a href="https://github.com/Microsoft/jericho">Jericho</a> -- Microsoft's open source <i>environment that connects learning agents with interactive fiction games.</i> Using the fabulous Frotz, of course.</li>
<li>
<a href="http://jeffe.cs.illinois.edu/teaching/algorithms/">Algorithms</a> -- new textbook from UIUC professor Jeff Erickson.</li>
<li>
<a href="https://www.edge.org/conversation/george_dyson-childhoods-end">The Digital Revolution Isn't Over, But Has Turned Into Something Else</a> (George Dyson) -- <i>The digital revolution began when stored-program computers broke the distinction between numbers that mean things and numbers that do things. Numbers that do things now rule the world. But who rules over the machines?</i> (via <a href="https://boingboing.net/2019/01/02/george-dyson-look-to-analog-s.html">BoingBoing</a>)</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-3-january-2019'>Four short links: 3 January 2019.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/x7kDcwA5KoI" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/KBS-792wg0g" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-3-january-2019http://feedproxy.google.com/~r/oreilly/radar/atom/~3/x7kDcwA5KoI/four-short-links-3-january-2019Four short links: 2 January 20192019-01-02T11:35:00Ztag:www.oreilly.com,2019-01-02:/ideas/four-short-links-2-january-2019<p><em>Robot Cafe, Surveillance Sci-Fi, Hardware is Hard, and UI Typeface</em></p><ol>
<li>
<a href="https://soranews24.com/2018/11/29/cafe-opens-in-tokyo-staffed-by-robots-controlled-by-paralyzed-people/">Tokyo Cafe Staffed by Robots Controlled by Paralyzed People</a> -- <i>Developed by Ory, a startup that specializes in robotics for disabled people, the OriHime-D is a 120 cm (4-foot) tall robot that can be operated remotely from a paralyzed person’s home. Even if the operator only has control of their eyes, they can command OriHime-D to move, look around, speak with people, and handle objects.</i> (via <a href="https://danhon.com/2019/01/01/bookmarks-for-december-29th-through-january-1st/">Dan Hon</a>)</li>
<li>
<a href="https://www.technologyreview.com/s/612590/the-reunion-a-new-science-fiction-story-about-surveillance-in-china/">The Reunion</a> -- <i>a new science fiction story about surveillance in China</i> by Chen Qiufan, published in MIT TR.</li>
<li>
<a href="https://spun.io/2018/12/15/lessons-from-running-a-small-scale-electronics-factory-in-my-guest-bedroom-part-1-design/">Lessons from Running a Small-Scale Electronics Factory in my Guest Bedroom</a> -- hardware is hard. Lots of things you only learn by getting amongst it.</li>
<li>
<a href="https://rsms.me/inter/">Inter UI</a> -- <i>a typeface specially designed for user interfaces with a focus on high legibility of small-to-medium sized text on computer screens.</i>
</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-2-january-2019'>Four short links: 2 January 2019.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/lCWk_JL7qGM" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/1B3RixMBwlM" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-2-january-2019http://feedproxy.google.com/~r/oreilly/radar/atom/~3/lCWk_JL7qGM/four-short-links-2-january-2019250+ live online training courses opened for January, February, and March2019-01-02T11:00:00Ztag:www.oreilly.com,2019-01-02:/ideas/250-plus-live-online-training-courses-opened-for-january-february-and-march<p><img src='https://d3ucjech6zwjp8.cloudfront.net/600x450/oreilly-insights-laptop-table-crop-0fee335635109441ea1b5fdb3401f403.jpg'/></p><p><em>Get hands-on training in Python, Java, machine learning, blockchain, and many other topics.</em></p><p>Learn new topics and refine your skills with <a href="https://learning.oreilly.com/live-training/">more than 250 new live online training courses we opened up for January, February, and March</a> on our online learning platform.</p>
<h2>AI and machine learning</h2>
<p><em><a href="https://learning.oreilly.com/live-training/courses/getting-started-with-chatbot-development-with-the-microsoft-bot-framework/0636920236320/">Getting Started with Chatbot Development with the Microsoft Bot Framework</a></em>, January 7-8</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/essential-machine-learning-and-exploratory-data-analysis-with-python-and-jupyter-notebook/0636920224556/">Essential Machine Learning and Exploratory Data Analysis with Python and Jupyter Notebook</a></em>, January 7-8</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/managed-machine-learning-systems-and-internet-of-things/0636920224778/">Managed Machine Learning Systems and Internet of Things</a></em>, January 9-10</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/machine-learning-in-practice/0636920234388">Machine Learning in Practice</a></em>, January 15</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/deep-learning-fundamentals/0636920225133">Deep Learning Fundamentals</a></em>, January 17</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/practical-mqtt-for-the-internet-of-things/0636920239369">Practical MQTT for the Internet of Things</a></em>, January 17-18</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/natural-language-processing-nlp-from-scratch/0636920226239">Natural Language Processing (NLP) from Scratch</a></em>, January 22</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/getting-started-with-machine-learning/0636920233701">Getting Started with Machine Learning</a></em>, January 24</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/artificial-intelligence-for-robotics/0636920230205">Artificial Intelligence for Robotics</a></em>, January 24-25</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/machine-learning-in-python-and-jupyter-for-beginners/0636920231080">Machine Learning in Python and Jupyter for Beginners</a></em>, January 30</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/protecting-data-privacy-in-a-machine-learning-world/0636920241997">Protecting Data Privacy in a Machine Learning World</a></em>, January 31</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/artificial-intelligence-real-world-applications/0636920225522">Artificial Intelligence: Real-World Applications</a></em>, January 31</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/beginning-machine-learning-with-scikit-learn/0636920247371">Beginning Machine Learning with scikit-learn</a></em>, February 4</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/what-you-need-to-know-about-data-science/0636920244202/">What You Need to Know About Data Science</a></em>, February 4</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/hands-on-chatbot-and-conversational-ui-development/0636920227397">Hands-On Chatbots and Conversational UI Development</a></em>, February 4-5</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/deep-learning-for-natural-language-processing-nlp/0636920253181">Deep Learning for Natural Language Processing (NLP)</a></em>, February 6</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/building-a-deep-learning-model-using-tensorflow/0636920247173">Building a Deep Learning Model Using Tensorflow</a></em>, February 7-8</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/building-a-robust-machine-learning-pipeline/0636920236344">Building a Robust Machine Learning Pipeline</a></em>, February 7-8</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/intermediate-machine-learning-with-scikit-learn/0636920247272">Intermediate Machine Learning with scikit-learn</a></em>, February 11</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/developing-a-data-science-project/0636920244189/">Developing a Data Science Project</a></em>, February 11</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/a-practical-introduction-to-machine-learning/0636920247517">A Practical Introduction to Machine Learning</a></em>, February 13</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/active-learning/0636920240532/">Active Learning</a></em>, February 13</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/deep-learning-with-tensorflow/0636920226567">Deep Learning with TensorFlow</a></em>, February 14</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/getting-started-with-machine-learning/0636920233756">Getting Started with Machine Learning</a></em>, February 21</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/artificial-intelligence-for-big-data/0636920233633/">Artificial Intelligence for Big Data</a></em>, February 26-27</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/deploying-machine-learning-models-to-production-a-toolkit-for-real-world-success/0636920253365/">Deploying Machine Learning Models to Production: A Toolkit for Real-World Success</a></em>, February 27-28</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/applied-deep-learning-for-coders-with-apache-mxnet/0636920252047/">Applied Deep Learning for coders with Apache MXNet</a></em>, March 4-5</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/essential-machine-learning-and-exploratory-data-analysis-with-python-and-jupyter-notebook/0636920228066/">Essential Machine Learning and Exploratory Data Analysis with Python and Jupyter Notebook</a></em>, March 4-5</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/managed-machine-learning-systems-and-internet-of-things/0636920228288/">Managed Machine Learning Systems and Internet of Things</a></em>, March 6-7</p>
<h2>Blockchain</h2>
<p><em><a href="https://learning.oreilly.com/live-training/courses/spotlight-on-innovation-how-blockchain-will-change-your-business-with-alison-mccauley/0636920253044/">Spotlight on Innovation: How Blockchain Will Change Your Business</a></em>, January 9</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/blockchain-applications-and-smart-contracts/0636920245513">Blockchain Applications and Smart Contracts</a></em>, January 11</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/introducing-blockchain/0636920234265">Introducing Blockchain</a></em>, January 22</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/ibm-blockchain-platform-as-a-service/0636920242857">IBM Blockchain Platform as a Service</a></em>, January 23-24</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/certified-blockchain-solutions-architect-cbsa-certification-crash-course/0636920248552">Certified Blockchain Solutions Architect (CBSA) Certification Crash Course</a></em>, January 25</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/building-smart-contracts-on-the-blockchain/0636920239970">Building Smart Contracts on the Blockchain</a></em>, January 31-February 1</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/managing-your-manager/0636920255062/">Managing your Manager</a></em>, February 13</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/salary-negotiation-fundamentals/0636920252757/">Salary Negotiation Fundamentals</a></em>, February 20</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/blockchain-and-cryptocurrencies-for-beginners/0636920240693/">Blockchain and Cryptocurrencies for Beginners</a></em>, February 21-22</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/blockchain-applications-and-smart-contracts/0636920245568/">Blockchain Applications and Smart Contracts</a></em>, February 27</p>
<h2>Business</h2>
<p><em><a href="https://learning.oreilly.com/live-training/courses/building-the-courage-to-take-risks/0636920236252">Building the Courage to Take Risks</a></em>, January 8</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/fundamentals-of-cognitive-biases/0636920232780">Fundamentals of Cognitive Biases</a></em>, January 14</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/negotiation-fundamentals/0636920242888">Negotiation Fundamentals</a></em>, January 17</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/emotional-intelligence-in-the-workplace/0636920240068">Emotional Intelligence in the Workplace</a></em>, January 22</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/writing-user-stories/0636920229957">Writing User Stories</a></em>, January 23</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/adaptive-project-management/0636920236276">Adaptive Project Management</a></em>, January 24</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/business-strategy-fundamentals/0636920233107">Business Strategy Fundamentals</a></em>, January 24</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/introduction-to-time-management-skills/0636920226079">Introduction to Time Management Skills</a></em>, January 25</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/having-difficult-conversations/0636920235446">Having Difficult Conversations</a></em>, January 28</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/the-power-of-lean-in-software-projects-less-wasted-effort-and-more-product-results/0636920242291">The Power of Lean in Software Projects: Less Wasted Effort and More Product Results</a></em>, January 29</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/research-sprints/0636920242116/">Research Sprints</a></em>, January 29</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/giving-a-powerful-presentation/0636920225089">Giving a Powerful Presentation</a></em>, January 30</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/tools-for-the-digital-transformation/0636920239277">Tools for the Digital Transformation</a></em>, January 30-31</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/managing-your-manager/0636920233473">Managing Your Manager</a></em>, January 31</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/introduction-to-critical-thinking/0636920226680">Introduction to Critical Thinking</a></em>, February 6</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/how-to-give-great-presentations/0636920220657">How to Give Great Presentations</a></em>, February 7</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/introduction-to-strategic-thinking-skills/0636920247135">Introduction to Strategic Thinking Skills</a></em>, February 11</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/your-first-30-days-as-a-manager/0636920235491">Your First 30 Days as a Manager</a></em>, February 12</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/building-your-people-network/0636920251439">Building Your People Network</a></em>, February 13</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/empathy-at-work/0636920248583">Empathy at Work</a></em>, February 13</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/60-minutes-to-designing-a-better-powerpoint-slide/0636920248613">60 Minutes to Designing a Better PowerPoint Slide</a></em>, February 14</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/product-management-in-90-minutes/0636920254560/">Product Management in 90 Minutes</a></em>, February 14</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/applying-critical-thinking/0636920240761/">Applying Critical Thinking</a></em>, February 19</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/managing-team-conflict/0636920240860/">Managing Team Conflict</a></em>, February 19</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/agile-for-everybody/0636920254591/">Agile for Everybody</a></em>, February 20</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/navigating-change/0636920252870/">Navigating Change</a></em>, February 20</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/having-difficult-conversations/0636920236696/">Having Difficult Conversations</a></em>, March 4</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/adaptive-project-management/0636920248903/">Adaptive Project Management</a></em>, March 6</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/why-smart-leaders-fail/0636920236733/">Why Smart Leaders Fail</a></em>, March 7</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/introduction-to-time-management-skills/0636920228479/">Introduction to Time Management Skills</a></em>, March 8</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/building-the-courage-to-take-risks/0636920249092/">Building the Courage to Take Risks</a></em>, March 8</p>
<h2>Data science and data tools</h2>
<p><em><a href="https://learning.oreilly.com/live-training/courses/apache-hadoop-spark-and-big-data-foundations/0636920233947">Apache Hadoop, Spark, and Big Data Foundations</a></em>, January 15</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/python-data-handling-a-deeper-dive/0636920249313">Python Data Handling - A Deeper Dive</a></em>, January 22</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/practical-data-science-with-python/0636920232957">Practical Data Science with Python</a></em>, January 22-23</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/time-series-forecasting/0636920240587">Time Series Forecasting</a></em>, January 23</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/hands-on-introduction-to-apache-hadoop-and-spark-programming/0636920233992">Hands-On Introduction to Apache Hadoop and Spark Programming</a></em>, January 23-24</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/cleaning-data-at-scale/0636920233121">Cleaning Data at Scale</a></em>, January 24</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/foundational-data-science-with-r/0636920243144">Foundational Data Science with R</a></em>, January 30-31</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/introduction-to-dax-using-power-bi/0636920249474">Introduction to DAX Using Power BI</a></em>, February 1</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/getting-started-with-alteryx/0636920244073/">Getting Started with Alteryx</a></em>, February 11-12</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/managing-enterprise-data-strategies-with-hadoop-spark-and-kafka-full-day/0636920252535">Managing Enterprise Data Strategies with Hadoop, Spark, and Kafka</a></em>, February 13</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/the-power-of-creating-visualizations-with-qlik-sense/0636920253082/">The Power of Creating Visualizations with Qlik Sense</a></em>, February 14-15</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/sql-fundamentals-for-data/0636920227199">SQL Fundamentals for Data</a></em>, February 19-20</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/building-distributed-pipelines-for-data-science-using-kafka-spark-and-cassandra/0636920227106">Building Distributed Pipelines for Data Science Using Kafka, Spark, and Cassandra</a></em>, February 19-21</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/apache-hadoop-spark-and-big-data-foundations/0636920235972/">Apache Hadoop, Spark and Big Data Foundations</a></em>, February 21</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/medium-r-programming-beyond-the-basics/0636920246961/">Medium R Programming</a></em>, February 25-26</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/intermediate-sql-for-data-analysis/0636920227267/">Intermediate SQL for Data Analysis</a></em>, February 27</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/visualization-and-presentation-of-data/0636920248637/">Visualization and Presentation of Data</a></em>, February 28</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/data-structures-in-java/0636920254751/">Data Structures in Java</a></em>, February 28</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/building-intelligent-bots-in-python/0636920236771/">Building Intelligent Bots in Python</a></em>, March 7</p>
<h2>Design</h2>
<p><em><a href="https://learning.oreilly.com/live-training/courses/fundamentals-of-ux-mapping/0636920242529/">Fundamentals of UX Mapping</a></em>, February 4-5</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/how-to-create-compelling-visuals-and-3d-content-with-3ds-max-and-v-ray/0636920230977/">How to Create Compelling Visuals and 3d Content with 3ds Max and V-Ray</a></em>, February 6</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/introduction-to-ui-ux-design/0636920248675/">Introduction to UI and UX Design</a></em>, February 25</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/design-thinking-for-non-designers/0636920257141/">Design Thinking for Non-Designers</a></em>, February 28</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/principles-of-conversation-design/0636920257240/">Principles of Conversation Design</a></em>, February 28</p>
<h2>Programming</h2>
<p><em><a href="https://learning.oreilly.com/live-training/courses/reactive-spring-boot/0636920239628">Reactive Spring Boot</a></em>, January 7</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/design-patterns-in-java/0636920239666">Design Patterns in Java</a></em>, January 7-8</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/spring-boot-and-kotlin/0636920239734">Spring Boot and Kotlin</a></em>, January 8</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/ground-zero-programming-with-javascript/0636920239574">Ground Zero Programming with JavaScript</a></em>, January 8</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/solid-principles-of-object-oriented-and-agile-design/0636920239789">SOLID Principles of Object-Oriented and Agile Design</a></em>, January 11</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/fundamentals-of-rust/0636920239345">Fundamentals of Rust</a></em>, January 14-15</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/mastering-c-game-development/0636920239253">Mastering C++ Game Development</a></em>, January 14-15</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/mastering-selinux/0636920234333">Mastering SELinux</a></em>, January 15</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/java-r-full-throttle-with-paul-deitel-a-one-day-code-intensive-java-standard-edition-presentation/0636920235347">Java Full Throttle with Paul Deitel: A One-Day, Code-Intensive Java Standard Edition Presentation</a></em>, January 15</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/discovering-modern-java/0636920248484">Discovering Modern Java</a></em>, January 16</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/introduction-to-android-application-development-with-kotlin/0636920239383">Introduction to Android Application Development with Kotlin</a></em>, January 17-18</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/learn-linux-in-3-hours/0636920231042">Learn Linux in 3 Hours</a></em>, January 18</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/scala-core-programming-methods-classes-and-traits/0636920225300">Scala Core Programming: Methods, Classes Traits</a></em>, January 22</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/programming-with-java-lambdas-and-streams/0636920249511">Programming with Java Lambdas and Streams</a></em>, January 22</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/getting-started-with-python-3/0636920254423">Getting Started with Python 3</a></em>, January 22-23</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/getting-started-with-node-js/0636920240150">Getting Started with Node.js</a></em>, January 23</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/quantitative-trading-with-python/0636920240556/">Quantitative Trading with Python</a></em>, January 23</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/mastering-the-basics-of-relational-sql-querying/0636920234791">Mastering the Basics of Relational SQL Querying</a></em>, January 23-24</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/developing-modern-react-patterns/0636920239604">Developing Modern React Patterns</a></em>, January 24</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/getting-started-with-spring-and-spring-boot/0636920233206">Getting Started with Spring and Spring Boot</a></em>, January 24-25</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/building-data-apis-with-graphql-1-day-class/0636920240204">Building Data APIs with GraphQL</a></em>, January 28</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/getting-started-with-react-js/0636920240242">Getting Started with React.js</a></em>, January 28</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/functional-programming-in-java/0636920233510">Functional Programming in Java</a></em>, January 28-29</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/julia-1-0-essentials/0636920234067">Julia 1.0 Essentials</a></em>, January 30</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/reactive-spring-and-spring-boot/0636920233435">Reactive Spring and Spring Boot</a></em>, January 30</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/programming-with-data-python-and-pandas/0636920252269/">Programming with Data: Python and Pandas</a></em>, February 4</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/beginning-r-programming/0636920245025">Beginning R Programming</a></em>, February 4-5</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/sql-for-any-it-professional/0636920245476/">SQL for Any IT Professional</a></em>, February 5</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/advanced-react-js/0636920244585">Advanced React.JS</a></em>, February 6</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/react-beyond-the-basics-master-react-s-advanced-concepts/0636920243212">React Beyond the Basics - Master React's Advanced Concepts</a></em>, February 7</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/advanced-sql-series-relational-division/0636920236122">Advanced SQL Series: Relational Division</a></em>, February 7</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/reactive-spring-boot/0636920240426/">Reactive Spring Boot</a></em>, February 7</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/scala-beyond-the-basics/0636920226611">Scala: Beyond the Basics</a></em>, February 7-8</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/basic-android-development/0636920235767">Basic Android Development</a></em>, February 7-8</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/object-oriented-programming-in-c-and-net-core/0636920247418">Object Oriented Programming in C# and .NET Core</a></em>, February 8</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/introduction-to-python-programming/0636920248842/">Introduction to Python Programming</a></em>, February 11</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/scala-fundamentals-from-core-concepts-to-real-code-in-5-hours/0636920232742/">Scala Fundamentals: From Core Concepts to Real Code in 5 Hours</a></em>, February 11</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/developing-incremental-architecture/0636920244677">Developing Incremental Architecture</a></em>, February 11-12</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/beginning-frontend-development-with-react/0636920246619">Beginning Frontend Development with React</a></em>, February 11-12</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/mastering-c-8-0-and-net-core-3-0/0636920244493">Mastering C# 8.0 and .NET Core 3.0</a></em>, February 11-12</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/getting-started-with-pandas/0636920231264">Getting Started with Pandas</a></em>, February 12</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/css-layout-fundamentals-from-floats-to-flexbox-and-css-grid/0636920244806">CSS Layout Fundamentals: From Floats to Flexbox and CSS Grid</a></em>, February 12</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/advanced-sql-series-proximal-and-linear-interpolations/0636920235538">Advanced SQL Series: Proximal and Linear Interpolations</a></em>, February 12</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/c-programming-a-hands-on-guide/0636920248538/">C# Programming: A Hands-on Guide</a></em>, February 12</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/getting-started-with-python-3/0636920231400">Getting Started with Python 3</a></em>, February 12-13</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/red-hat-certified-engineer-rhce-crash-course/0636920231516/">Red Hat Certified Engineer (RHCE) Crash Course</a></em>, February 12-15</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/mastering-pandas/0636920231479">Mastering Pandas</a></em>, February 13</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/getting-started-with-java-from-core-concepts-to-real-code-in-4-hours/0636920226819/">Getting Started with Java: From Core Concepts to Real Code in 4 Hours</a></em>, February 14</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/kotlin-for-android/0636920235835">Kotlin for Android</a></em>, February 14-15</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/fundamentals-of-iot-with-javascript/0636920227922">Fundamentals of IoT with JavaScript</a></em>, February 14-15</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/clean-code/0636920240655/">Clean Code</a></em>, February 15</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/modern-java-exception-handling/0636920232803/">Modern Java Exception Handling</a></em>, February 15</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/advanced-sql-series-window-functions/0636920236405">Advanced SQL Series: Window Functions</a></em>, February 19</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/concurrency-in-python/0636920253709/">Concurrency in Python</a></em>, February 19</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/reactive-programming-with-java-completable-futures/0636920240372/">Reactive Programming with Java Completable Futures</a></em>, February 19</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/getting-started-with-go/0636920242383/">Getting Started with Go</a></em>, February 19-20</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/fundamentals-of-functional-programming-with-examples-in-scala/0636920230038">Fundamentals of Functional Programming - With Examples in Scala</a></em>, February 20-21</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/modern-application-development-with-c-and-net-core/0636920227458">Modern Application Development with C# and .NET Core</a></em>, February 21-22</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/advanced-kubernetes-in-practice/0636920239321/">Advanced Kubernetes in Practice</a></em>, February 21-22</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/next-generation-java-testing-with-junit-5/0636920234166/">Next-Generation Java Testing with JUnit 5</a></em>, February 25</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/ground-zero-programming-with-javascript/0636920240938/">Ground Zero Programming with JavaScript</a></em>, February 25</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/whats-new-in-java/0636920234210/">What's New In Java</a></em>, February 26</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/design-patterns-in-java/0636920240808/">Design Patterns in Java</a></em>, February 26-27</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/automating-go-projects/0636920253266/">Automating Go Projects</a></em>, February 28</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/java-programming-crash-course-including-features-from-java-9-to-11/0636920251804/">Java Programming Crash Course: Including Features from Java 9 to 11</a></em>, February 28</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/modern-javascript/0636920226284/">Modern JavaScript</a></em>, March 20</p>
<h2>Security</h2>
<p><em><a href="https://learning.oreilly.com/live-training/courses/introduction-to-ethical-hacking-and-penetration-testing/0636920249405">Introduction to Ethical Hacking and Penetration Testing</a></em>, January 8-9</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/comptia-network-crash-course/0636920235255">CompTIA Network+ Crash Course</a></em>, January 16-18</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/introduction-to-encryption/0636920230892">Introduction to Encryption</a></em>, January 22</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/aws-security-fundamentals/0636920242154">AWS Security Fundamentals</a></em>, January 28</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/cissp-crash-course/0636920225874">CISSP Crash Course</a></em>, January 29-30</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/professional-sql-server-high-availability-and-disaster-recovery/0636920232933">Professional SQL Server High Availability and Disaster Recovery</a></em>, January 29-30</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/comptia-pentest-crash-course/0636920234104">CompTIA PenTest+ Crash Course</a></em>, January 30-31</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/comptia-cybersecurity-analyst-cysa-cs0-001-crash-course/0636920240464/">CompTIA Cybersecurity Analyst CySA+ CS0-001 Crash Course</a></em>, February 4-5</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/certified-ethical-hacker-ceh-crash-course/0636920236054/">Certified Ethical Hacker (CEH) Crash Course</a></em>, February 5-6</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/defensive-cybersecurity-fundamentals/0636920248514">Defensive Cyber Security Fundamentals</a></em>, February 12</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/security-for-machine-learning/0636920242031">Security for Machine Learning</a></em>, February 13</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/cyber-security-fundamentals/0636920236634/">Cyber Security Fundamentals</a></em>, February 14-15</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/aws-certified-security-specialty-crash-course/0636920227045">AWS Certified Security - Specialty Crash Course</a></em>, February 19-20</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/ethical-hacking-bootcamp-with-hands-on-labs/0636920227304/">Ethical Hacking Bootcamp with Hands-on Labs</a></em>, February 19-21</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/intense-introduction-to-hacking-web-applications/0636920236016/">Intense Introduction to Hacking Web Applications</a></em>, February 21</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/security-operation-center-soc-best-practices/0636920249795/">Security Operation Center (SOC) Best Practices</a></em>, February 25</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/cissp-crash-course/0636920227991/">CISSP Crash Course</a></em>, February 26-27</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/cissp-certification-practice-questions-and-exam-strategies/0636920236580/">CISSP Certification Practice Questions and Exam Strategies</a></em>, February 27</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/comptia-cloud-cv0-002-exam-prep/0636920228127/">CompTIA Cloud+ CV0-002 Exam Prep</a></em>, March 5</p>
<h2>Systems engineering and operations</h2>
<p><em><a href="https://learning.oreilly.com/live-training/courses/introduction-to-kubernetes/0636920245117">Introduction to Kubernetes</a></em>, January 3-4</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/aws-certified-cloud-practitioner-exam-crash-course/0636920252931">AWS Certified Cloud Practitioner Exam Crash Course</a></em>, January 7-8</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/red-hat-certified-system-administrator-rhcsa-crash-course/0636920230403">Red Hat Certified System Administrator (RHCSA) Crash Course</a></em>, January 7-10</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/creating-serverless-apis-with-aws-lambda-and-api-gateway/0636920242994">Creating Serverless APIs with AWS Lambda and API Gateway</a></em>, January 8</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/microservice-fundamentals/0636920248330/">Microservice Fundamentals</a></em>, January 10</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/amazon-web-services-aws-up-and-running/0636920243038">Amazon Web Services (AWS): Up and Running</a></em>, January 11</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/getting-started-with-openshift/0636920244295">Getting Started with OpenShift</a></em>, January 11</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/building-a-deployment-pipeline-with-jenkins-2/0636920243076">Building a Deployment Pipeline with Jenkins 2</a></em>, January 14-15</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/microservices-architecture-and-design/0636920245735">Microservices Architecture and Design</a></em>, January 16-17</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/aws-certified-solutions-architect-associate-crash-course/0636920234425">AWS Certified Solutions Architect Associate Crash Course</a></em>, January 16-17</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/google-cloud-platform-gcp-for-aws-professionals/0636920244257">Google Cloud Platform (GCP) for AWS Professionals</a></em>, January 18</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/red-hat-rhel-8-new-features/0636920248712">Red Hat RHEL 8 New Feature</a></em>, January 22</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/rethinking-rest-a-hands-on-guide-to-graphql-and-queryable-apis/0636920249368">Rethinking REST: A Hands-On Guide to GraphQL and Queryable APIs</a></em>, January 22</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/docker-beyond-the-basics-ci-cd/0636920225775">Docker: Beyond the Basics (CI &amp; CD)</a></em>, January 22-23</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/domain-driven-design-and-event-driven-microservices/0636920244516">Domain-Driven Design and Event-Driven Microservices</a></em>, January 22-23</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/chaos-engineering-planning-designing-and-running-automated-chaos-experiments/0636920223115">Chaos Engineering: Planning, Designing, and Running Automated Chaos Experiments</a></em>, January 23</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/architecture-for-continuous-delivery/0636920254331">Architecture for Continuous Delivery</a></em>, January 23</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/building-and-managing-kubernetes-applications/0636920246237">Building and Managing Kubernetes Applications</a></em>, January 24</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/continuous-deployment-to-kubernetes/0636920227793">Continuous Deployment to Kubernetes</a></em>, January 24-25</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/api-driven-architecture-with-swagger-and-api-blueprint/0636920230175">API Driven Architecture with Swagger and API Blueprint</a></em>, January 25</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/microservice-decomposition-patterns/0636920248378">Microservice Decomposition Patterns</a></em>, January 25</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/microservices-caching-strategies/0636920246411/">Microservices Caching Strategies</a></em>, January 28</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/the-devops-toolkit/0636920227854">DevOps Toolkit</a></em>, January 28-29</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/end-to-end-containerization-with-amazon-ecs/0636920233275">End-to-End Containerization with Amazon ECS</a></em>, January 28-30</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/ansible-in-4-hours/0636920230526">Ansible in 4 Hours</a></em>, January 29</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/comptia-cloud-cv0-002-exam-prep/0636920225560">CompTIA Cloud+ CV0-002 Exam Prep</a></em>, January 29</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/amazon-web-services-aws-managed-services/0636920251187">Amazon Web Services: AWS Managed Services</a></em>, January 29-30</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/cissp-certification-practice-questions-and-exam-strategies/0636920234302">CISSP Certification Practice Questions and Exam Strategies</a></em>, January 30</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/comparing-service-based-architectures/0636920254386">Comparing Service-Based architectures</a></em>, January 31</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/automation-with-aws-serverless-technologies/0636920244103">Automation with AWS Serverless Technologies</a></em>, February 1</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/managing-containers-on-linux/0636920239505/">Managing Containers on Linux</a></em>, February 1</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/bootiful-testing/0636920239550/">Bootiful Testing</a></em>, February 4</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/linux-under-the-hood/0636920231134/">Linux Under the Hood</a></em>, February 4</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/aws-monitoring-strategies/0636920226529">AWS Monitoring Strategies</a></em>, February 4-5</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/ccnp-r-s-switch-300-115-crash-course/0636920234647">CCNP R/S SWITCH (300-115) Crash Course</a></em>, February 4-6</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/9-steps-to-awesome-with-kubernetes/0636920231363/">9 Steps to Awesome with Kubernetes</a></em>, February 5</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/linux-performance-optimization/0636920231172/">Linux Performance Optimization</a></em>, February 5</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/scalable-concurrency-with-the-java-executor-framework/0636920240327/">Scalable Concurrency with the Java Executor Framework</a></em>, February 5</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/getting-started-with-amazon-web-services-aws/0636920234500/">Getting Started with Amazon Web Services (AWS)</a></em>, February 5-6</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/linux-troubleshooting-advanced-linux-techniques/0636920231219/">Linux Troubleshooting: Advanced Linux Techniques</a></em>, February 6</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/microservice-collaboration/0636920253235/">Microservice Collaboration</a></em>, February 6</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/from-developer-to-software-architect/0636920243267">From Developer to Software Architect</a></em>, February 6-7</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/building-applications-with-apache-cassandra/0636920247753">Building Applications with Apache Cassandra</a></em>, February 6-7</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/analyzing-software-architecture/0636920246527/">Analyzing Software Architecture</a></em>, February 7</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/istio-on-kubernetes-enter-the-service-mesh/0636920231318/">Istio on Kubernetes: Enter the Service Mesh</a></em>, February 7</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/amazon-web-services-aws-technical-essentials/0636920251255/">Amazon Web Services (AWS) Technical Essentials</a></em>, February 7</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/ansible-for-managing-network-devices/0636920226482/">Ansible for Managing Network Devices</a></em>, February 7</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/moving-from-server-side-to-client-side-with-angular/0636920246909">Moving from Server-Side to Client-Side with Angular</a></em>, February 7-8</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/google-cloud-certified-associate-cloud-engineer-crash-course/0636920246343/">Google Cloud Certified Associate Cloud Engineer Crash Course</a></em>, February 7-8</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/managing-complexity-in-network-engineering/0636920252115">Managing Complexity in Network Engineering</a></em>, February 8</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/aws-access-management/0636920248798/">AWS Access Management</a></em>, February 8</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/getting-started-with-openshift/0636920248156/">Getting Started with OpenShift</a></em>, February 11</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/docker-up-and-running/0636920226857">Docker: Up and Running</a></em>, February 12-13</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/practical-docker/0636920254041/">Practical Docker</a></em>, February 14</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/microservice-fundamentals/0636920256380/">Microservice Fundamentals</a></em>, February 15</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/architecture-for-continuous-delivery/0636920254829/">Architecture for Continuous Delivery </a></em>, February 19</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/comparing-service-based-architectures/0636920255017/">Comparing Service-Based Architectures</a></em>, February 20</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/aws-design-fundamentals/0636920251309/">AWS Design Fundamentals</a></em>, February 21-22</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/aws-certified-cloud-practitioner-crash-course/0636920234579/">AWS Certified Cloud Practitioner Crash Course</a></em>, February 21-22</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/hands-on-multi-cloud-for-developers/0636920249870/">Hands-On Multi-Cloud for Developers</a></em>, February 25-26</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/google-cloud-platform-professional-cloud-architect-certification-crash-course/0636920245919/">Google Cloud Platform Professional Cloud Architect Certification Crash Course</a></em>, February 25-26</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/implementing-infrastructure-as-code/0636920247463/">Implementing Infrastructure as Code</a></em>, February 26</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/microservices-architecture-and-design/0636920246541/">Microservices Architecture and Design</a></em>, February 26-27</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/building-micro-frontends/0636920243571/">Building Micro-Frontends</a></em>, February 28</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/quality-of-service-qos-for-cisco-routers-and-switches/0636920236443/">Quality of Service (QoS) for Cisco Routers and Switches</a></em>, February 28</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/linux-filesystem-administration/0636920236498/">Linux Filesystem Administration</a></em>, March 4-5</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/introduction-to-kubernetes/0636920246152/">Introduction to Kubernetes</a></em>, March 4-5</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/building-a-cloud-roadmap/0636920228233/">Building a Cloud Roadmap</a></em>, March 6</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/domain-driven-design-and-event-driven-microservices/0636920246435/">Domain-Driven Design and Event-Driven Microservices</a></em>, March 6-7</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/understanding-aws-cloud-compute-options/0636920236917/">Understanding AWS Cloud Compute Options</a></em>, March 7-8</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/microservice-decomposition-patterns/0636920255956/">Microservice Decomposition Patterns</a></em>, March 8</p>
<h2>Web programming</h2>
<p><em><a href="https://learning.oreilly.com/live-training/courses/modern-web-development-with-typescript-and-angular/0636920232988">Modern Web Development with TypeScript and Angular</a></em>, January 22-23</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/developing-modern-react-patterns/0636920240983/">Developing Modern React Patterns</a></em>, February 28</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/modern-web-development-with-typescript-and-angular/0636920235705/">Modern Web Development with TypeScript and Angular</a></em>, March 5-6</p>
<p><em><a href="https://learning.oreilly.com/live-training/courses/professional-front-end-application-development-with-react/0636920255819/">Professional Front-end Application Development with React</a></em>, March 7-8</p>
<p>Continue reading <a href='https://www.oreilly.com/ideas/250-plus-live-online-training-courses-opened-for-january-february-and-march'>250+ live online training courses opened for January, February, and March.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/kYx252f2mew" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/B114wWwTQPA" height="1" width="1" alt=""/>https://www.oreilly.com/ideas/250-plus-live-online-training-courses-opened-for-january-february-and-marchhttp://feedproxy.google.com/~r/oreilly/radar/atom/~3/kYx252f2mew/250-plus-live-online-training-courses-opened-for-january-february-and-marchFour short links: 1 January 20192019-01-01T15:40:00Ztag:www.oreilly.com,2019-01-01:/ideas/four-short-links-1-january-2019<p><em>Amazon Tricks, Public Domain, Blocking Telegram, and Approximate Spreadsheets</em></p><ol>
<li>
<a href="https://www.theverge.com/2018/12/19/18140799/amazon-marketplace-scams-seller-court-appeal-reinstatement">Amazon Marketplace Scams</a> -- <i>As Amazon has escalated its war on fake reviews, sellers have realized that the most effective tactic is not buying them for yourself, but buying them for your competitors—the more obviously fraudulent the better. A handful of glowing testimonials, preferably in broken English about unrelated products and written by a known review purveyor on Fiverr, can not only take out a competitor and allow you to move up a slot in Amazon’s search results, it can land your rival in the bewildering morass of Amazon’s suspension system.</i> (via <a href="https://marginalrevolution.com/marginalrevolution/2018/12/amazon-war-evolution-private-law.html">Marginal Revolution</a>)</li>
<li>
<a href="https://publicdomainreview.org/collections/class-of-2019/">Growing Public Domain</a> -- the public domain now includes <i>"In the Orchard" and "Mrs Dalloway in Bond Street," by Virginia Woolf; "The Ego and the Id," by Sigmund Freud (original German version); "Towards a New Architecture," by Le Corbusier (original French version);
"The Murder of Roger Ackroyd" and "The Murder on the Links," by Agatha Christie; "The Lurking Fear," by H.P. Lovecraft; "Duino Elegies," by Rainer Maria Rilke (original German version); "Safety Last!" and "Why Worry?," by Harold Lloyd; M. C. Escher—"Dolphins"; Pablo Picasso—"The Pipes of Pan" and "Paulo on a Donkey"; and Paul Klee—"Architecture, Tightrope Walker, and Masks."</i>
</li>
<li>
<a href="https://media.ccc.de/v/35c3-9653-russia_vs_telegram_technical_notes_on_the_battle">Russia vs. Telegram: Technical Notes on the Battle</a> -- a CCC talk. Spoiler alert: Russia didn't succeed, and in trying, they <i>also banned IP addresses of major local businesses (VKontakte, Yandex, and others), presumably, by mistake. A flaw in the filter was exploited to bring one of the major ISPs down for a while. Moscow internet exchange point announced that a like flaw of the filter could be used to disrupt peering.</i>
</li>
<li>
<a href="https://www.getguesstimate.com/">Guesstimate</a> -- <a href="https://github.com/getguesstimate/guesstimate-app">open source</a> <i>spreadsheet for things that aren’t certain</i> where you can <i>create Fermi estimates and perform Monte Carlo estimates</i>. I've linked to this before, but I hadn't realized it's open source. Development has slowed, the founders are <a href="https://news.ycombinator.com/item?id=18786639">busy elsewhere</a>, but it's a promising idea.</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-1-january-2019'>Four short links: 1 January 2019.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/T6WcQ6xOcOA" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/x4hvlWXBDUU" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-1-january-2019http://feedproxy.google.com/~r/oreilly/radar/atom/~3/T6WcQ6xOcOA/four-short-links-1-january-2019Four short links: 31 December 20182018-12-31T12:55:00Ztag:www.oreilly.com,2018-12-31:/ideas/four-short-links-31-december-2018<p><em>Schema Crawler, Open Source Bug Bounties, Essential C, and AI Poker</em></p><ol>
<li>
<a href="https://www.schemacrawler.com/index.html">SchemaCrawler</a> -- <i>Free database schema discovery and comprehension tool.</i> Make sense of the databases you inherit.</li>
<li>
<a href="https://www.zdnet.com/article/eu-to-fund-bug-bounty-programs-for-14-open-source-projects-starting-january-2019/">EU To Fund Bug Bounties for Open Source Projects</a> (ZD Net) -- this is good, but insufficient. See <a href="https://twitter.com/k8em0/status/1079499330275270656">Katie Moussouris</a>.</li>
<li>
<a href="http://cslibrary.stanford.edu/101/EssentialC.pdf">Essential C</a> -- a sweet little summary of C, an even terser K&amp;R.</li>
<li>
<a href="https://www.youtube.com/watch?v=b7bStIQovcY">AI, Game Theory, and Poker</a> (YouTube) -- a talk by Tuomas Sandholm, CMU professor and <i>co-creator of Libratus, which is the first AI system to beat top human players at the game of Heads-Up No-Limit Texas Hold'em</i>. From the <a href="https://lexfridman.com/tuomas-sandholm/">AI Podcast</a>.</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-31-december-2018'>Four short links: 31 December 2018.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/4SUsnVMesVc" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/8JchH0VmorE" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-31-december-2018http://feedproxy.google.com/~r/oreilly/radar/atom/~3/4SUsnVMesVc/four-short-links-31-december-2018Four short links: 28 December 20182018-12-28T12:55:00Ztag:www.oreilly.com,2018-12-28:/ideas/four-short-links-28-december-2018<p><em>Bayes Notes, Fake Internet, Tensorflow Privacy, Sortable UUIDs</em></p><ol>
<li>
<a href="https://jrnold.github.io/bayesian_notes/">Updating: A Set of Bayesian Notes</a> -- <i>Notes on Bayesian methods - written to supplement CS&amp;SS/STAT 564: Bayesian Statistics for the Social Sciences.</i>
</li>
<li>
<a href="http://nymag.com/intelligencer/2018/12/how-much-of-the-internet-is-fake.html">How Much of the Internet is Fake?</a> (NY Mag) -- <i>What’s gone from the internet, after all, isn’t “truth,” but trust: the sense that the people and things we encounter are what they represent themselves to be.</i>
</li>
<li>
<a href="https://github.com/tensorflow/privacy">TensorFlow Privacy</a> -- <i>Library for training machine learning models with privacy for training data</i>.</li>
<li>
<a href="https://github.com/ulid/spec">Universally Unique Lexicographically Sortable Identifiers</a> -- <i>128-bit compatibility with UUID; 1.21e+24 unique ULIDs per millisecond; Lexicographically sortable!; Canonically encoded as a 26 character string, as opposed to the 36 character UUID; Uses Crockford's base32 for better efficiency and readability (5 bits per character); Case insensitive; No special characters (URL safe); Monotonic sort order (correctly detects and handles the same millisecond).</i>
</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-28-december-2018'>Four short links: 28 December 2018.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/iwFTCIpIvSA" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/VBxNKxIjkbc" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-28-december-2018http://feedproxy.google.com/~r/oreilly/radar/atom/~3/iwFTCIpIvSA/four-short-links-28-december-2018Four short links: 27 December 20182018-12-27T12:45:00Ztag:www.oreilly.com,2018-12-27:/ideas/four-short-links-27-december-2018<p><em>Reading Minds, Year Gotchas, LSTM Conversation, and Fast Scanning</em></p><ol>
<li>
<a href="http://news.mit.edu/2018/mit-picower-neurotechnology-provides-real-time-readouts-where-rats-think-they-are-1218">Reading Rats' Minds</a> (MIT) -- <i>In recent years, scientists have shown that by recording the electrical activity of groups of neurons in key areas of the brain, they could read a rat’s thoughts of where it was, both after it actually ran the maze and also later when it would dream of running the maze in its sleep—a key process in consolidating its memory. In the new study, several of the scientists involved in pioneering such mind-reading methods now report they can read out those signals in real time as the rat runs the maze, with a high degree of accuracy and the ability to account for the statistical relevance of the readings almost instantly after they are made. [...] The software of the system is <a href="https://github.com/yuehusile/real_time_read_out_GPU">open source</a> and available for fellow neuroscientists to download and use freely, Chen and Wilson say.</i> Rats not included. <a href="https://www.cell.com/cell-reports/fulltext/S2211-1247(18)31796-0">The paper</a> is open access, too.</li>
<li>
<a href="https://ericasadun.com/2018/12/25/iso-8601-yyyy-yyyy-and-why-your-year-may-be-wrong/">yyyy and YYYY: Why Your Year May Be Wrong</a> (Erica Sadun) -- <i>The presence of YYYY in the date format without its expected supporting information reduces to “start of year, go back one week, report the first day.” (I’ll explain this more in just a little bit.)</i>
</li>
<li>
<a href="https://lexfridman.com/juergen-schmidhuber/">Conversation with Juergen Schmidhuber</a> -- <i>the co-creator of long short-term memory networks (LSTMs) that are used in billions of devices today for speech recognition, translation, and much more. ... The history of science is the history of compression progress.</i> Metalearning, self-referential programs, and more. It's a dry discussion of fiery ideas. (via <a href="https://twitter.com/hardmaru/status/1077740853811019777">hardmaru</a>)</li>
<li>
<a href="http://www.k2.t.u-tokyo.ac.jp/vision/BFS-Auto/">Scanning 250 Pages/Minute</a> -- <i>Our system continuously observes 3D deformation of each flipped page at 500 times per second and recognizes the best moment for book image digitization</i>. The video is hypnotic. (via <a href="https://twitter.com/Reza_Zadeh/status/1076585561568075777">Reza Zadeh</a>)</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-27-december-2018'>Four short links: 27 December 2018.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/mubHKjU39a8" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/7IfiJmm5QqQ" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-27-december-2018http://feedproxy.google.com/~r/oreilly/radar/atom/~3/mubHKjU39a8/four-short-links-27-december-2018Four short links: 26 December 20182018-12-26T14:40:00Ztag:www.oreilly.com,2018-12-26:/ideas/four-short-links-26-december-2018<p><em>Evil FizzBuzz, Atari OS, Logic Guide, and Artificial Life</em></p><ol>
<li>
<a href="https://twitter.com/jasongorman/status/1077118717572640768">Evil FizzBuzz</a> (Jason Gorman) -- a really clever CI exercise for a team.</li>
<li>
<a href="https://github.com/emutos/emutos">EmuTOS</a> -- open source reimplementation of the original Atari ST operating system. (via <a href="https://news.ycombinator.com/item?id=18751080">Hacker News</a>)</li>
<li>
<a href="https://www.logicmatters.net/resources/pdfs/TeachYourselfLogic2017.pdf">Teach Yourself Logic: A Study Guide</a> -- a wonderfully chatty book that functions as an introduction to logic for mathematicians and philosophers.</li>
<li>
<a href="https://arxiv.org/abs/1812.05433">Lenia: Biology of Artificial Life</a> -- <i>a new model of artificial life called Lenia (from Latin lenis "smooth"), a two-dimensional cellular automaton with continuous space-time-state and generalized local rule. Computer simulations show that Lenia supports a great diversity of complex autonomous patterns or "lifeforms" bearing resemblance to real-world microscopic organisms. More than 400 species in 18 families have been identified, many discovered via interactive evolutionary computation. They differ from other cellular automata patterns in being geometric, metameric, fuzzy, resilient, adaptive, and rule-generic.</i> <a href="https://chakazul.github.io/Lenia/JavaScript/Lenia.html">Implementation</a> with <a href="https://github.com/Chakazul/Lenia">source</a>.</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-26-december-2018'>Four short links: 26 December 2018.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/n4yPjZZEUzs" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/5IrK3tozUlY" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-26-december-2018http://feedproxy.google.com/~r/oreilly/radar/atom/~3/n4yPjZZEUzs/four-short-links-26-december-2018Four short links: 25 December 20182018-12-25T11:00:00Ztag:www.oreilly.com,2018-12-25:/ideas/four-short-links-25-december-2018<p><em>Hardware Testing is Hard, Biological Keygen, Christmas Robots, and Open Data</em></p><ol>
<li>
<a href="https://www.bunniestudios.com/blog/?p=5450">Maxclave</a> (Bunnie Huang) -- you thought software testing was hard? Welcome to the world of hardware testing.</li>
<li>
<a href="https://onlinelibrary.wiley.com/doi/full/10.1002/adts.201800154">Biological One-Way Functions for Secure Key Generation</a> -- <i>It is demonstrated that the spatiotemporal dynamics of an ensemble of living organisms such as T cells can be used for maximum entropy, high‐density, and high‐speed key generation.</i>
</li>
<li>
<a href="https://spectrum.ieee.org/automaton/robotics/robotics-hardware/video-friday-happy-robot-holidays-2018">Christmas Robot Roundup</a> (IEEE) -- selection of holiday greetings from various robots and robotics companies. I for one welcome our new tinsel-and-holly-clad industrial apparatus overlords.</li>
<li>
<a href="https://e-pluribusunum.org/2018/12/21/congress-made-open-government-data-the-default-in-the-united-states/">Congress Votes to Make Open Government Data the Default in the United States</a> -- <i>The Open, Public, Electronic, and Necessary Government Data Act (AKA the OPEN Government Data Act) is about to become law [...]. This codifies two canonical principles for democracy in the 21st century: 1. public information should be open by default to the public in a machine-readable format, where such publication doesn’t harm privacy or security. 2. federal agencies should use evidence when they make public policy.</i> Merry Christmas, democracy; here's a small present in a bad year.</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-25-december-2018'>Four short links: 25 December 2018.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/-2QG44QDJ5w" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/-nbPnjXBuA0" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-25-december-2018http://feedproxy.google.com/~r/oreilly/radar/atom/~3/-2QG44QDJ5w/four-short-links-25-december-2018Four short links: 24 December 20182018-12-24T11:50:00Ztag:www.oreilly.com,2018-12-24:/ideas/four-short-links-24-december-2018<p><em>Learning Prolog, Data Race, Animating Photos, and Easy Flashing</em></p><ol>
<li>
<a href="https://xmonader.github.io/prolog/2018/12/21/solving-murder-prolog.html">Solving Murder with Prolog</a> -- if THIS was the motivating example for Prolog, I'd have taken to it a lot sooner! I love those logic puzzle books.</li>
<li>
<a href="https://sloanreview.mit.edu/article/the-machine-learning-race-is-really-a-data-race/">The Machine Learning Race is Really a Data Race</a> (MIT Sloan Review) -- <i>Organizations that hope to make AI a differentiator need to draw from alternative data sets—ones they may have to create themselves.</i>
</li>
<li>
<a href="https://grail.cs.washington.edu/projects/wakeup/">Photo Wakeup: 3-D Character Animation from a Single Photo</a> -- this is incredible work. Watch <a href="https://www.youtube.com/watch?v=G63goXc5MyU">the video</a> if nothing else.</li>
<li>
<a href="https://www.balena.io/etcher/">Etcher</a> -- <i>Flash OS images to SD cards and USB drives, safely and easily.</i> <a href="https://github.com/balena-io/etcher">Open source</a>.</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-24-december-2018'>Four short links: 24 December 2018.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/_BPVu4maMAQ" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/1DopbUOqUiU" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-24-december-2018http://feedproxy.google.com/~r/oreilly/radar/atom/~3/_BPVu4maMAQ/four-short-links-24-december-2018Four short links: 21 December 20182018-12-21T13:45:00Ztag:www.oreilly.com,2018-12-21:/ideas/four-short-links-21-december-2018<p><em>Tech in China, Wisdom of Small Groups, iOS VPN, and Gameboy Supercomputer</em></p><ol>
<li>
<a href="https://www.technologyreview.com/magazine/2019/01/">MIT TR: The China Issue</a> -- from AI to landscaping, it's the state of big tech in China.</li>
<li>
<a href="https://arxiv.org/abs/1703.00045##">Aggregated Knowledge From a Small Number of Debates Outperforms the Wisdom of Large Crowds</a> -- what it says on the box. This is why I like the <a href="http://www.theworldcafe.com/key-concepts-resources/world-cafe-method/">World Cafe Method</a> of facilitating discussions.</li>
<li>
<a href="https://lists.zx2c4.com/pipermail/wireguard/2018-December/003694.html">Wireguard for iOS</a> -- a port of Wireguard VPN to the Apple mobile ecosystem.</li>
<li>
<a href="https://towardsdatascience.com/a-gameboy-supercomputer-33a6955a79a4">A Gameboy Supercomputer</a> -- <i>At a total of slightly over one billion frames per second, it is arguably the fastest 8-bit game console cluster in the world.</i>
</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-21-december-2018'>Four short links: 21 December 2018.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/hsFk6pRr7bc" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/rILUcyC13Hs" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-21-december-2018http://feedproxy.google.com/~r/oreilly/radar/atom/~3/hsFk6pRr7bc/four-short-links-21-december-2018Trends in data, machine learning, and AI2018-12-20T13:00:00Ztag:www.oreilly.com,2018-12-20:/ideas/trends-in-data-machine-learning-and-ai<p><img src='https://d3ucjech6zwjp8.cloudfront.net/600x450/looking-653449_1920_crop-daf5fb0ac427c06ba32d7dd389db669e.jpg'/></p><p><em>The O’Reilly Data Show Podcast: Ben Lorica looks ahead at what we can expect in 2019 in the big data landscape.</em></p><p>For the end-of-year holiday episode of the <a href="https://www.oreilly.com/ideas/topics/oreilly-data-show-podcast">Data Show</a>, I turned the tables on Data Show host Ben Lorica to talk about trends in big data, machine learning, and AI, and what to look for in 2019. Lorica also showcased some highlights from our upcoming <a href="https://conferences.oreilly.com/strata">Strata Data</a> and <a href="https://conferences.oreilly.com/artificial-intelligence">Artificial Intelligence conferences</a>.</p><p>Continue reading <a href='https://www.oreilly.com/ideas/trends-in-data-machine-learning-and-ai'>Trends in data, machine learning, and AI.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/gaAi7xUd8AY" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/As2OSyf1B3A" height="1" width="1" alt=""/>Jenn Webbhttps://www.oreilly.com/ideas/trends-in-data-machine-learning-and-aihttp://feedproxy.google.com/~r/oreilly/radar/atom/~3/gaAi7xUd8AY/trends-in-data-machine-learning-and-aiFour short links: 20 December 20182018-12-20T12:10:00Ztag:www.oreilly.com,2018-12-20:/ideas/four-short-links-20-december-2018<p><em>Misinformation Research, AI UI, Facebook's Value, and Python Governance</em></p><ol>
<li>
<a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3273111">Common-Knowledge Attacks on Democracy</a> -- <i>We argue that scaling up computer security arguments to the level of the state, so that the entire polity is treated as an information system with associated attack surfaces and threat models, provides the best immediate way to understand these attacks and how to mitigate them. We demonstrate systematic differences between how autocracies and democracies work as information systems, because they rely on different mixes of common and contested political knowledge.</i> Released 17 November; Bruce Schneier is co-author.</li>
<li>
<a href="https://www.nngroup.com/articles/machine-learning-ux/">Can Users Control and Understand a UI Driven by Machine Learning?</a> -- <i>In this article, we examine some of the challenges users encounter when interacting with machine learning algorithms on Facebook, Instagram, Google News, Netflix, and Uber Driver.</i>
</li>
<li>
<a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0207101">Estimating the Value of Facebook by Paying Users to Stop Using It</a> -- <i>across all three samples, the mean bid to deactivate Facebook for a year exceeded $1,000.</i>
</li>
<li>
<a href="https://lwn.net/SubscriberLink/775105/5db16cfe82e78dc3/">Python Gets a New Governance Model</a> -- <i>The council is imbued with "broad authority to make decisions about the project," but the goal is that it uses that authority rarely; it is meant to delegate its authority broadly. The PEP says the council should seek consensus, rather than dictate, and that it should define a standard PEP decision-making process that will (hopefully) rarely need council votes to resolve. It is, however, the "court of final appeal" for decisions affecting the language. But the council cannot change the governance PEP; that can only happen via a two-thirds vote of the core team.</i> Python gets a constitution (aka <a href="https://www.python.org/dev/peps/pep-8016/">PEP 8016</a>).</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-20-december-2018'>Four short links: 20 December 2018.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/E7CLWSAp7H8" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/yOK_fHrx2nE" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-20-december-2018http://feedproxy.google.com/~r/oreilly/radar/atom/~3/E7CLWSAp7H8/four-short-links-20-december-2018What is neural architecture search?2018-12-20T12:00:00Ztag:www.oreilly.com,2018-12-20:/ideas/what-is-neural-architecture-search<p><img src='https://d3ucjech6zwjp8.cloudfront.net/600x450/neurons-582050_1920_crop-627194068bc0df7a644359ff60cb180e.jpg'/></p><p><em>An overview of NAS and a discussion on how it compares to hyperparameter optimization.</em></p><p>Deep learning offers the promise of bypassing the process of manual feature engineering by learning representations in conjunction with statistical models in an end-to-end fashion. However, neural network architectures themselves are typically designed by experts in a painstaking, ad hoc fashion. Neural architecture search (NAS) has been touted as the path forward for alleviating this pain by automatically identifying architectures that are superior to hand-designed ones.</p>
<p>But with the field moving so fast both in terms of research progress and hype, it can be hard to get answers to basic questions: What exactly is NAS and is it fundamentally different from AutoML or hyperparameter optimization? Do specialized NAS methods actually work? Aren't they prohibitively expensive to use? Should I be using specialized NAS methods? In this post, we'll answer each of these questions. Our discussion touches upon a few key points:</p>
<ul>
<li>There is a false dichotomy between NAS and traditional hyperparameter optimization; in fact, NAS is a subset of hyperparameter optimization. Moreover, specialized NAS methods are not actually fully automated, as they rely on human-designed architectures as starting points.</li>
<li>While exploring and tuning different neural network architectures is of crucial importance in developing high-quality deep learning applications, in our view specialized NAS methods are not ready for primetime just yet: they introduce significant algorithmic and computational complexities compared to high-quality hyperparameter optimization algorithms (e.g., <a href="https://blog.ml.cmu.edu/2018/12/12/massively-parallel-hyperparameter-optimization">ASHA</a>) without demonstrating improved performance on standard benchmarking tasks.</li>
<li>Specialized NAS methods have nonetheless exhibited remarkable advances in the past few years in terms of improved accuracy, reduced computational costs, and architecture size, and could eventually surpass human performance on neural architecture design.</li>
</ul>
<p>To set the stage, let's first discuss how NAS fits within the wider umbrella of AutoML (automated machine learning).</p>
<h2>AutoML ⊃ hyperparameter optimization ⊃ NAS</h2>
<figure class="center" id="id-6nGie"><img alt="NAS, autoML, hyperparameter optimization" src="https://d3ansictanv2wj.cloudfront.net/image1-3250b5283382455a64a9b3fabc39f6c2.jpg"><figcaption><span class="label">Figure 1. </span>Image courtesy of Determined AI.</figcaption></figure>
<p>AutoML focuses on automating every aspect of the machine learning (ML) workflow to increase efficiency and democratize machine learning so that non-experts can apply machine learning to their problems with ease. While AutoML encompasses the automation of a wide range of problems associated with ETL (extract, transform, load), model training, and model deployment, the problem of hyperparameter optimization is a core focus of AutoML. This problem involves configuring the internal settings that govern the behavior of an ML model/algorithm in order to return a high-quality predictive model.</p>
<p>For example, ridge regression models require setting the value of a regularization term, random forest models require the user to set the maximum tree depth and minimum number of samples per leaf, and training any model with stochastic gradient descent requires setting an appropriate step size. Neural networks also require setting a multitude of hyperparameters, including (1) selecting an optimization method along with its associated set of hyperparameters; (2) setting the dropout rate and other regularization hyperparameters; and, if desired, (3) tuning parameters that control the architecture of the network (e.g., number of hidden layers, number of convolutional filters).</p>
<p>Although the exposition on NAS might suggest it is a completely new problem, our final example above hints at a close relationship between hyperparameter optimization and NAS. While the search spaces used for NAS are generally larger and control different aspects of the neural network architecture, the underlying problem is the same as that addressed by hyperparameter optimization: find a configuration within the search space that performs well on the target task. Hence, we view NAS to be a subproblem within hyperparameter optimization.</p>
<p>NAS is nonetheless an exciting direction to study, as focusing on a specialized subproblem provides the opportunity to exploit additional structure to design custom tailored solutions, as is done by many specialized NAS approaches. In the next section, we will provide an overview of NAS and delve more into the similarities and differences between hyperparameter optimization and NAS.</p>
<h2>NAS overview</h2>
<figure class="center" id="id-RMkiq"><img alt="Neural architecture search (NAS) overview" src="https://d3ansictanv2wj.cloudfront.net/image2-1ab1292fe731b686511b26381162e4d9.png"><figcaption><span class="label">Figure 2. </span>Image courtesy of Determined AI.</figcaption></figure>
<p>Interest in NAS ballooned after the work of <a href="https://arxiv.org/abs/1611.01578">Zoph, et. al., 2016</a> used reinforcement learning to design, at the time, state-of-the-art architectures for image recognition and language modeling. However, Zoph, et. al., 2016, in addition to other first generation specialized approaches for NAS, required a tremendous amount of computational power (e.g., hundreds of GPUs running for thousands (!) of GPU days in aggregate), making them impractical for all but the likes of companies like Google. More recent approaches exploit various methods of reuse to drastically reduce the computational cost, and new methods are being rapidly introduced in the research community.</p>
<p>We'll next dive a bit deeper into the core design decisions associated with all of these specialized NAS methods (for a detailed overview of NAS, we recommend the excellent <a href="https://arxiv.org/abs/1808.05377">survey by Elsken, et al., 2017</a>). The three main components are:</p>
<ol>
<li>
<strong>Search space.</strong> This component describes the set of possible neural network architectures to consider. These search spaces are designed specific to the application—e.g., a space of convolutional networks for computer vision tasks or a space of recurrent networks for language modeling tasks. Hence, NAS methods are not fully automated, as the design of these search spaces fundamentally relies on human-designed architectures as starting points. Even so, there are still many architectural decisions remaining. In fact, the number of possible architectures considered in these search spaces are often over 10^10.</li>
<li>
<strong>Optimization method.</strong> This component determines how to explore the search space in order to find a good architecture. The most basic approach here is random search, while various adaptive methods have also been introduced—e.g., reinforcement learning, evolutionary search, gradient-based optimization, and Bayesian optimization. While these adaptive approaches differ in how they determine which architectures to evaluate, they all attempt to bias the search toward architectures that are more likely to perform well. Unsurprisingly, all of these methods have counterparts that have been introduced in the context of traditional hyperparameter optimization tasks.</li>
<li>
<strong>Evaluation method.</strong> This component measures the quality of each architecture considered by the optimization method. The simplest, but most computationally expensive choice is to fully train an architecture. One can alternatively exploit partial training, similar in spirit to early-stopping methods commonly used in hyperparameter optimization like ASHA. NAS-specific evaluation methods—such as network morphism, weight-sharing, and hypernetworks—have also been introduced to exploit the structure of neural networks to provide cheaper, heuristic estimates of quality. Partial training approaches are typically an order-of-magnitude cheaper than full training, while NAS-specific evaluation methods are two to three orders of magnitude cheaper than full training.</li>
</ol>
<p>Notably, these are the same three requisite ingredients for traditional hyperparameter optimization methods. The research community has converged on a few canonical benchmarking data sets and tasks to evaluate the performance of different search methods, and we'll next use these benchmarks to report results on head-to-head comparisons between (1) human-designed architectures tuned via hyperparameter optimization methods, and (2) NAS-designed architectures identified via leading specialized NAS methods. (NAS focuses on the problem of <em>identifying</em> architectures, but nonetheless requires a secondary hyperparameter optimization step to tune the non-architecture-specific hyperparameters of the architecture it identifies. Our results show the test error after performing both steps.)</p>
<h2>NAS models vs. human-designed models</h2>
<p>The two most common tasks used to benchmark NAS methods are (1) designing convolutional neural network (CNN) architectures evaluated on the CIFAR-10 data set, and (2) designing recurrent neural network (RNN) architectures evaluated on the PennTree Bank (PTB) data set. We show the test error for different architectures on CIFAR-10 in the table below.</p>
<table>
<tbody>
<tr>
<th></th>
<th>Source</th>
<th>Number of Parameters (Millions)</th>
<th>Test Error</th>
<th>Search Method</th>
<th>Evaluation Method</th>
</tr>
<tr>
<td>PyramidNet + ShakeDrop</td>
<td><a href="https://openreview.net/pdf?id=S1NHaMW0b">Yamada et al., 2018</a></td>
<td>26</td>
<td>2.31</td>
<td>Human designed</td>
<td>-</td>
</tr>
<tr>
<td>NASNet-A + cutout</td>
<td><a href="https://arxiv.org/abs/1707.07012">Zoph et al., 2017</a></td>
<td>3.3</td>
<td>2.65</td>
<td>Reinforcement Learning</td>
<td>Full Train</td>
</tr>
<tr>
<td>AmoebaNet-B + cutout</td>
<td><a href="https://arxiv.org/abs/1802.01548v4">Real et al., 2018</a></td>
<td>34.9</td>
<td>2.13</td>
<td>Evolutionary</td>
<td>Full Train</td>
</tr>
<tr>
<td>NAONET</td>
<td><a href="https://arxiv.org/abs/1808.07233">Luo et al., 2018</a></td>
<td>28.6</td>
<td>2.98</td>
<td>Gradient</td>
<td>Partial Train</td>
</tr>
<tr>
<td>DARTS + cutout</td>
<td><a href="https://arxiv.org/abs/1806.09055">H. Liu et al., 2018</a></td>
<td>3.4</td>
<td>2.83</td>
<td>Gradient</td>
<td>Weight Sharing</td>
</tr>
</tbody>
<caption>
<span class="label">Table 1. </span>Test error on CIFAR-10 for leading architectures either human designed or via specialized NAS methods with various search and evaluation methods. Note that all architectures were tuned via standard hyperparameter optimization methods.</caption>
</table>
<p>For the CIFAR-10 benchmark, specialized NAS methods that use full training perform comparably to manually designed architectures; however, they are prohibitively expensive and take more than 1,000 GPU days. Although methods that exploit partial training or other NAS-specific evaluation methods require less computation to perform the search (400 GPU days and ~1 GPU day, respectively), they are outperformed by the manually designed architecture in Table 1. Notably, the NAS architectures have nearly an order of magnitude fewer parameters than the human-designed model, indicating promising applications of NAS to memory- and latency-constrained settings.</p>
<p>The test perplexity for different architectures on the PTB data set are shown in Table 2.</p>
<table>
<tbody>
<tr>
<th></th>
<th>Source</th>
<th>Test Perplexity</th>
<th>Search Method</th>
<th>Evaluation Method</th>
</tr>
<tr>
<td>LSTM with MoS</td>
<td><a href="https://arxiv.org/abs/1711.03953">Yang et al., 2017</a></td>
<td>54.4</td>
<td>Human designed</td>
<td>-</td>
</tr>
<tr>
<td>NASNet</td>
<td><a href="https://arxiv.org/abs/1611.01578">Zoph et al., 2016</a></td>
<td>62.4</td>
<td>Reinforcement Learning</td>
<td>Full Train</td>
</tr>
<tr>
<td>NAONET</td>
<td><a href="https://arxiv.org/abs/1808.07233">Luo et al., 2018</a></td>
<td>56.0</td>
<td>Gradient</td>
<td>Partial Train</td>
</tr>
<tr>
<td>DARTS</td>
<td><a href="https://arxiv.org/abs/1806.09055">H. Liu et al., 2018</a></td>
<td>55.7</td>
<td>Gradient</td>
<td>Weight Sharing</td>
</tr>
</tbody>
<caption>
<span class="label">Table 2. </span>Test perplexity on PTB for leading architectures either designed by humans or via specialized NAS methods with various search and evaluation methods. Note that all architectures were tuned via standard hyperparameter optimization methods.</caption>
</table>
<p>The specialized NAS results are less competitive on the PTB benchmark compared to manually designed architectures. It is surprising, however, that cheaper evaluation methods outperform full training on this benchmark; this is likely due to the additional advances that have been made in training LSTMs since the publication of Zoph, et.al., 2016.</p>
<h2>Are specialized NAS methods ready for widespread adoption?</h2>
<p>Not yet! To be clear, exploring various architectures and performing extensive hyperparameter optimization remain crucial components of any deep learning application workflow. However, in light of the existing research results (as highlighted above), we believe that while specialized NAS methods have demonstrated promising results on these two benchmarks, they are still not ready for prime time for the following reasons:</p>
<ol>
<li>Since highly tuned, manually designed architectures are competitive with computationally tractable NAS methods on CIFAR-10 and outperform specialized NAS methods on PTB, we believe resources are better spent on hyperparameter optimization of existing manually designed architectures.</li>
<li>Most specialized NAS methods are fairly specific to a given search space and need to be retrained or retooled for each new search space. Additionally, certain approaches suffer from robustness issues and can be hard to train. These issues currently hinder the general applicability of existing specialized NAS methods to different tasks.</li>
</ol>
<p><strong>Related:</strong></p>
<ul>
<li><a href="https://www.oreilly.com/ideas/toward-the-jet-age-of-machine-learning">"Toward the Jet Age of machine learning"</a></li>
<li><a href="https://www.oreilly.com/ideas/neuroevolution-a-different-kind-of-deep-learning">"Neuroevolution: A different kind of deep learning"</a></li>
<li><a href="https://www.oreilly.com/ideas/deep-automation-in-machine-learning">"Deep automation in machine learning"</a></li>
<li>Ameet Talwalkar on <a href="https://www.oreilly.com/ideas/how-to-train-and-deploy-deep-learning-at-scale">"How to train and deploy deep learning at scale"</a>
</li>
<li><a href="https://www.oreilly.com/ideas/open-endedness-the-last-grand-challenge-youve-never-heard-of">"Open-endedness: The last grand challenge you've never heard of"</a></li>
</ul>
<p>Continue reading <a href='https://www.oreilly.com/ideas/what-is-neural-architecture-search'>What is neural architecture search?.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/Tz2ZNeTeXP4" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/zy7DygX2398" height="1" width="1" alt=""/>Liam Li, Ameet Talwalkarhttps://www.oreilly.com/ideas/what-is-neural-architecture-searchhttp://feedproxy.google.com/~r/oreilly/radar/atom/~3/Tz2ZNeTeXP4/what-is-neural-architecture-searchFour short links: 19 December 20182018-12-19T15:55:00Ztag:www.oreilly.com,2018-12-19:/ideas/four-short-links-19-december-2018<p><em>Observable Notebooks, Disinformation Report, Chained Blocking, and Trivia from 2018</em></p><ol>
<li>
<a href="https://beta.observablehq.com/">Observable Notebooks</a> -- JavaScript notebooks. (via <a href="https://24ways.org/2018/observable-notebooks-and-inaturalist/">Observable Notebooks and iNaturalist</a>)</li>
<li>
<a href="https://www.newknowledge.com/disinforeport">Disinformation Report</a> -- selective amplification (or pre-consumption filtering) remains one of the most interesting open challenges in infotech, and this report gives context and urgency to it. <i>The IRA shifted a majority of its activity to Instagram in 2017; this was perhaps in response to increased scrutiny on other platforms, including media coverage of its Twitter operation. Instagram engagement outperformed Facebook</i>. New Knowledge note that the Russian misinformation agency was <i>run like a digital marketing shop [...] They built their content using digital marketing best practices, even evolving page logos and typography over time.</i>. (via <a href="https://twitter.com/noUpside/status/1074676769452638210">Renee DiResta</a>)</li>
<li>
<a href="https://chrome.google.com/webstore/detail/twitter-block-chain/dkkfampndkdnjffkleokegfnibnnjfah/overview">Twitter Block Chain</a> -- a Chrome extension that blocks followers of the jerk, not just the jerk themselves. The power of the open web is that we can write the tools the platforms don't yet provide, however clunky. (via <a href="https://twitter.com/hadyngreen/status/1075106037605183488">Hadyn Green</a>)</li>
<li>
<a href="https://medium.com/fluxx-studio-notes/52-things-i-learned-in-2018-b07fc110d8e1">52 Things I Learned in 2018</a> -- each comes with attribution. Three sample facts, sans attribution: <i>(*) 35% of Rwanda’s national blood supply outside the capital city is now delivered by drone. (*) [Unicode] includes a group of ‘ghost characters’ (妛挧暃椦槞蟐袮閠駲墸壥彁) which have no known meaning. It’s believed they are errors introduced by folds and wrinkles during a paper-based 1978 Japanese government project to standardize the alphabet, but are now locked into the standard forever. (*) Cassidy Williams had a dream about a Scrabble-themed mechanical keyboard. When she woke up, she started cold-calling Hasbro to ask for permission to make it real. Eventually, she made it happen.</i>
</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-19-december-2018'>Four short links: 19 December 2018.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/eJXMprdPlqk" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/hxTO1Z4spIw" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-19-december-2018http://feedproxy.google.com/~r/oreilly/radar/atom/~3/eJXMprdPlqk/four-short-links-19-december-2018Deep automation in machine learning2018-12-19T12:00:00Ztag:www.oreilly.com,2018-12-19:/ideas/deep-automation-in-machine-learning<p><img src='https://d3ucjech6zwjp8.cloudfront.net/600x450/library_of_congress_crop-87c1cf12380f150b62d2673eb2aaf580.jpg'/></p><p><em>We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline.</em></p><p>In a <a href="https://www.oreilly.com/ideas/what-machine-learning-means-for-software-development">previous post</a>, we talked about applications of machine learning (ML) to software development, which included a tour through sample tools in data science and for managing data infrastructure. Since that time, Andrej Karpathy has made some more predictions about the fate of software development: he envisions a <a href="https://medium.com/@karpathy/software-2-0-a64152b37c35">Software 2.0</a>, in which the nature of software development has fundamentally changed. Humans no longer implement code that solves business problems; instead, they define desired behaviors and train algorithms to solve their problems. As he writes, “a neural network is a better piece of code than anything you or I can come up with in a large fraction of valuable verticals.” We won’t be writing code to optimize scheduling in a manufacturing plant; we’ll be training ML algorithms to find optimum performance based on historical data.</p>
<p>If humans are no longer needed to write enterprise applications, what do we do? Humans are still needed to write software, but that software is of a different type. Developers of Software 1.0 have a large body of tools to choose from: IDEs, CI/CD tools, automated testing tools, and so on. The tools for Software 2.0 are only starting to exist; one big task over the next two years is developing the IDEs for machine learning, plus other tools for data management, pipeline management, data cleaning, data provenance, and data lineage.</p>
<p>Karpathy’s vision is ambitious, and we don’t think enterprise software developers need to worry about their jobs any time soon. However, it is clear that the way software is developed is changing. With machine learning, the challenge isn’t writing the code; the algorithms are implemented in a number of well-known and highly optimized libraries. We don’t need to implement our own versions of long short-term memory (LSTM) or reinforcement learning; we get that from <a href="https://pytorch.org/">PyTorch</a>, <a href="https://github.com/ray-project/ray">Ray RLlib</a>, or some other library. However, machine learning isn’t possible without data, and our tools for working with data aren’t adequate. We have great tools for working with code: creating it, managing it, testing it, and deploying it. But they don’t address the data side, and with ML, managing the data management as important as managing the code itself. GitHub is an excellent tool for managing code, but we need to think about [code+data]. There is no GitHub for data, though we are starting to see version control projects for machine learning models, such as <a href="https://dvc.org/">DVC</a>.</p>
<p>It’s important to think precisely about what <em>git</em> does. It captures source code, and all the changes to the source code. For any codebase, it can tell you where the code came from (provenance), and all the changes that led from the original commit to the version you downloaded. It’s capable of maintaining many different branches, reflecting different custom views of the code. If someone has changed a line of code, you will see that change, and who made it. And (with some human help and pain) it can resolve conflicting changes on different branches. Those capabilities are all important for data; but good as <em>git</em> is for code, it isn’t adequate for data. It has trouble with data that isn’t formatted as a sequence of lines (like source code), has problems with binary data, and it chokes on huge files. And it is ill-suited for tracking transformations that change every item in a data set, such as a matrix multiplication or normalization.</p>
<p>We also need better tools for collecting data. Given all the talk about the explosion of data, it’s ironic that most of the data that's exploding falls on the floor and is never captured. Data management isn’t limited to issues like provenance and lineage; one of the most important things you can do with data is collect it. Given the rate at which data is created, data collection has to be automated. How do you do that without dropping data? Given that the results produced by any model will reflect the data used to create the model, how do you ensure your data collection process is fair, representative, and unbiased?</p>
<h2>Toward a sustainable ML practice</h2>
<p>In our forthcoming report <em>Evolving Data Infrastructure</em>, one aspect we studied was what European organizations were doing to build a sustainable machine learning practice: not a proof of concept or a one-time cool idea to be dropped when the next technical fad comes along, but a permanent part of the organization’s plans. It’s one thing to kick the tires briefly; it’s something else entirely to deeply build the infrastructure needed to integrate machine learning into your organization.</p>
<figure class="center" id="id-68jin"><img alt="european organizations machine learning practice" src="https://d3ansictanv2wj.cloudfront.net/image1-79922db797cbb2bc90dce1b37cee75fc.png"></figure>
<p>Building a sustainable practice means investing in the tools that allow you to work effectively over the long term. These tools enable you to build software you can rely on, not just proof-of-concept hacks that don’t need to be duplicated. These tools include basics like ETL (extract, transform and load: extracting data from multiple sources, transforming it into a form that’s useful, and loading it into a datastore for analysis). It’s no surprise that companies are investing in data science platforms to run machine learning at scale, just as they invested in Hadoop a decade ago. And given that most of the work of a data scientist is cleaning the data prior to analysis, it’s no surprise that most companies are investing in tools for data preparation. These are tools we would have expected to see on the list five years ago as companies started building their data science practices.</p>
<p>We also see investment in new kinds of tools. Anomaly detection is well-known in the financial industry, where it’s frequently used to detect fraudulent transactions, but it can also be used to catch and fix data quality issues automatically. This isn’t surprising; if you’re collecting data from several weather stations and one of them malfunctions, you would expect to see anomalous data. A faulty weather station might stop reporting data (which might be turned into zeros, infinities, or nulls in your data stream), or it might just send readings that are a few degrees above what’s expected, or that are out of line with other stations in the area. In any case, there will be an anomaly in the input data, and it will be easier for a machine to detect that anomaly than a human. If you suddenly see unexpected patterns in your social data, that may mean adversaries are attempting to poison your data sources. Anomaly detection may have originated in finance, but it is becoming a part of every data scientist’s toolkit.</p>
<p>Metadata analysis makes it possible to build data catalogs, which in turn allow humans to discover data that’s relevant to their projects. Democratizing access to data is a major step on the process to becoming a data-driven (or an AI-driven) company; users must be empowered to explore data and to create their own projects. That is difficult without some kind of data catalog. You can tell users they have access to all the data they need, and given them access to databases, but unless they know what data is available and how to find it, that access doesn’t mean anything. Creating that catalog by hand isn’t possible; it needs to be <a href="https://conferences.oreilly.com/strata/strata-eu/public/schedule/detail/74218">automated</a>.</p>
<h2>Data lineage</h2>
<p>The history of data analysis has been plagued with a cavalier attitude toward data sources. That is ending; <a href="https://docs.google.com/document/d/13yUs1iRLotlaMZ55p5-KFzRe4Hwj7iZ3DKkqfnGR93I/edit?ts=5c00789d">discussions of data ethics</a> have made data scientists aware of the importance of <a href="https://stackoverflow.com/questions/43383197/what-are-the-differences-between-data-lineage-and-data-provenance/43386224#43386224">data lineage and provenance</a>. Both refer to the source of the data: where does the data come from, how was it gathered, and how was it modified along the way? Data provenance is increasingly a legal issue; it’s clearly important to know where data came from and how it was obtained. It’s particularly important when you’re combining data from multiple sources; we’ve often observed that data is most powerful when several sources are combined. Provenance can get very complex, particularly when results generated from one set of data are further combined with other data.</p>
<p>It’s important to be able to trace data lineage at a granular level, to understand the entire path from the source to the application. Data is modified all the time: it’s often been observed that most of the work in data science is cleanup or preparation. Data cleaning involves modifying the data: eliminating rows that have missing or illegal values, for example. We’re beginning to understand exactly how important it is to understand what happened during that cleanup, how data evolved from its raw state: that can be a source of error and bias. As companies ingest and use more data, and as the number of consumers of that data increases, it’s important to know the data is trustworthy. When data is modified, it’s important to track exactly how and when it was modified.</p>
<p>The tools for tracking data provenance and lineage are limited, although products from commercial vendors such as <a href="https://www.trifacta.com/data-lineage/">Trifacta</a> are starting to appear. Git and its predecessors (SVN and even RCS) can track every change to every line of code in software, maintain multiple branches of the code, and reconcile differences between branches. How do we do that for data? Furthermore, what will we do with the results? It's common to normalize data, or to transform in some way, but such transformations can easily change every byte in the data set.</p>
<p>Not only do such changes pose problems, but tools like git force humans to supply explanatory comments when they commit a new version to explain why any change was made. That's not possible with an automated data pipeline. It might be possible for systems to log and "explain" the changes they make, but this assumes you have fine-grained control to force them to do so.</p>
<p>Such control may be possible within the scope of a single tool. For example, Jacek Laskowski <a href="https://jaceklaskowski.gitbooks.io/mastering-apache-spark/spark-rdd-lineage.html">describes</a> how to extract a resilient distributed data set (RDD) lineage graph that describes a series of Spark transformations. This graph could be committed to a lineage tracking system, or even a more traditional version-control system, to document transformations that have been applied to the data. But this process only applies to a single machine learning platform: Spark. To be generally useful, every platform would need to support extracting a lineage graph, preferably in a single format and without requiring additional scripting by developers. That's a good vision for where we need to go, but we're not there yet.</p>
<p>Data provenance and lineage isn’t just about the quality of the results; it’s a security and compliance issue. At the <a href="https://www.safaribooksonline.com/videos/strata-data-conference/9781491976326/9781491976326-video314195">Strata Data Conference in New York in 2017</a>, danah boyd argued that social media systems were intentionally poisoned by tools that propagated low-quality content designed to sway the algorithms that determined what people watch. Malicious agents have learned to “hack the attention economy.” In "<a href="https://www.hoover.org/research/flat-light">Flat Light: Data Protection for the Disoriented, from Policy to Practice</a>," Andrew Burt and Daniel Geer argue that in the past, data accuracy was binary; data was either correct or incorrect. Now, data provenance is as important as correctness, if not more so. You can’t judge whether data is reliable if you don’t know its origin. For machine learning systems, this means we need to track source data as well as source code: the data used to train the system is as important to its behavior as the algorithms and their implementation.</p>
<p>We are starting to see some tools that automate data quality issues. Intuit uses the <a href="https://quickbooks-engineering.intuit.com/taming-data-quality-with-circuit-breakers-dbe550d3ca78">Circuit Breaker pattern</a> to halt data pipelines when they detect anomalies in the data. Their tool tracks data lineage because it’s important to understand the inputs and outputs of every stage of the pipeline; it also tracks the status of the pipeline components themselves and the quality of the data at every stage of the pipeline (is it within expected bounds, is it of the appropriate type, etc.). <a href="https://conferences.oreilly.com/strata/strata-eu/public/schedule/detail/74218">Intuit</a>, <a href="https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/73025">Netflix</a>, and <a href="https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/72299">Stitchfix</a> have built data lineage systems that track the origin and evolution of the data that they use in their systems.</p>
<h2>Automation is more than model building</h2>
<p>In the past year, we have seen several companies build tools to “automate machine learning,” including <a href="https://cloud.google.com/automl/">Google</a> and <a href="https://aws.amazon.com/sagemaker/">Amazon</a>. These tools automate the process of building models: trying different algorithms and topologies, to minimize error when the model is used on test data. But these tools just build models, and we’ve seen that machine learning requires much more. The model can’t exist without tools for data integration and ETL, data preparation, data cleaning, anomaly detection, data governance, and more. Automating model building is just one component of automating machine learning.</p>
<p>To be truly useful, automated machine learning has to go much deeper than model building. It’s too simple to think a machine learning project will require a single model; one project can easily require several different models, doing different things. And different aspects of the business, while superficially similar, can require different models, trained from different data sources. Consider a hotel business such as Marriott: more than 6,000 hotels, and more than $20 billion in gross revenue. Any hotel would like to predict occupancy, income, and the services they need to provide. But each hotel is a completely different business: The Times Square Marriott is dominated by large corporate conferences and New York City tourism, while the Fairfield Inn in Sebastopol is dominated by local events and wine country tourism. The customer demographics are different; but more than that, the event sources are different. The Sebastopol hotel needs to know about local weddings and wine country events; I’d expect them to use natural language processing to parse feeds from local newspapers. The Times Square hotel needs to know about Broadway openings, Yankees games, and Metro-North train schedules. This isn’t just a different model; these two businesses require completely different data pipelines. Automating the model building process is helpful, but it doesn’t go far enough.</p>
<p>Hotels aren’t the only business requiring more models than humans can conceivably build. Salesforce provides AI services for its clients, which number in the hundreds of thousands. Each client needs a custom model; models can’t be shared, even between clients in similar businesses. Aside from confidentiality issues, no two clients have the same customers or the same data, and small differences between clients can add up to large errors. Even with the most optimistic estimates for machine learning talent, there aren’t enough people to build that many models by hand. <a href="https://www.oreilly.com/ideas/building-tools-for-enterprise-data-science">Salesforce’s solution is TransmogrifAI</a>, an open source automated ML library for structured data. TransmogrifAI automates the model building process, like other Auto ML solutions, but it also <a href="https://engineering.salesforce.com/open-sourcing-transmogrifai-4e5d0e098da2">automates many other tasks</a>, including data preparation and feature validation.</p>
<p>Other enterprise software vendors are in the same boat: they have many customers, each of whom requires “custom models.” They cannot hire enough data scientists to support all of these customers with conventional manual workflows. Automation isn’t an option; it’s a necessity.</p>
<p>Automation doesn’t stop when the model is “finished”; in any real-world application, the model can never be considered “finished.” Any model’s performance will degrade over time: situations change, people change, products change, and the model may even play a role in driving that change. <a href="https://www.oreilly.com/ideas/we-need-to-build-machine-learning-tools-to-augment-machine-learning-engineers">We expect to see new tools for automating model testing</a>, either alerting developers when a model needs to be re-trained or starting the training process automatically. And we need to go even further: beyond simple issues of model accuracy, we need to test for <a href="https://www.oreilly.com/ideas/managing-risk-in-machine-learning">fairness and ethics</a>. Those tests can’t be automated completely, but tools can be developed to help domain experts and data scientists detect problems of fairness. For example, such a tool might generate an alert when it detects a potential problem, like a significantly higher loan rejection rate from a protected group; it might also provide tools to help a human expert analyze the problem and make a correction.</p>
<h2>Closing thoughts</h2>
<p>The way we build software is changing. Whether or not we get to Karpathy’s Software 2.0, we’re certainly on a road headed in that direction. The future holds more machine learning, not less; developing and maintaining models will be part of the job of building software. Software developers will be spending less time writing code and more time training models.</p>
<p>However, the lack of data—and of tools for working with data—remains a fundamental bottleneck. Over the past 50 years, we’ve developed excellent tools for working with software. We now need to build the tools for software+data: tools to track data provenance and lineage, tools to build catalogs from metadata, tools to do fundamental operations like ETL. Companies are investing in these foundational technologies.</p>
<p>The next bottleneck will be model building itself; the number of models we need will always be much greater than the number of people capable of building those models by hand. Again, the solution is building tools for automating the process. We need to do more than automate model building with autoML; we also need to automate feature engineering, data preparation, and other tasks at every stage of the data pipeline. Software developers are, after all, in the business of automation. And the most important thing for software developers to automate is their own work.</p>
<p><strong>Related content</strong></p>
<ul>
<li><a href="https://www.oreilly.com/ideas/what-machine-learning-means-for-software-development">“What machine learning means for software development”</a></li>
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</li>
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<p>Continue reading <a href='https://www.oreilly.com/ideas/deep-automation-in-machine-learning'>Deep automation in machine learning.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/HlriBehK36I" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/vO11klmyHDA" height="1" width="1" alt=""/>Ben Lorica, Mike Loukideshttps://www.oreilly.com/ideas/deep-automation-in-machine-learninghttp://feedproxy.google.com/~r/oreilly/radar/atom/~3/HlriBehK36I/deep-automation-in-machine-learning10 top AWS resources on O’Reilly’s online learning platform2018-12-19T11:00:00Ztag:www.oreilly.com,2018-12-19:/ideas/10-top-aws-resources-on-oreillys-online-learning-platform<p><img src='https://d3ucjech6zwjp8.cloudfront.net/600x450/network-3524352_1920_crop-421095ef5ec71b1e7b06552ea0e313de.jpg'/></p><p><em>Our most-used AWS resources will help you stay on track in your journey to learn and apply AWS.</em></p><p>We dove into the data on <a href="https://www.safaribooksonline.com/home/?utm_source=oreilly&amp;utm_medium=newsite&amp;utm_campaign=10-top-aws-resources-on-oreillys-online-learning-platform_body_link">our online learning platform</a> to identify the most-used Amazon Web Services (AWS) resources. These are the items our platform subscribers regularly turn to as they apply AWS in their projects and organizations.</p>
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<p><em><a href="https://www.safaribooksonline.com/videos/automation-in-aws/9780134818313?utm_source=oreilly&amp;utm_medium=newsite&amp;utm_campaign=10-top-aws-resources-on-oreillys-online-learning-platform_body_link"><strong>Automation in AWS with CloudFormation, CLI, and SDKs</strong></a></em> — Richard Jones covers AWS services and tools used to automate the creation and maintenance of AWS infrastructure, including VPC, EC2, Lambda, RDS, and deploying containerized microservices with Docker.</p>
<p><em><a href="https://www.safaribooksonline.com/library/view/aws-security-best/9781789134513/?utm_source=oreilly&amp;utm_medium=newsite&amp;utm_campaign=10-top-aws-resources-on-oreillys-online-learning-platform_body_link"><strong>AWS: Security Best Practices on AWS</strong></a></em> — Albert Anthony focuses on using native AWS security features and managed AWS services to help you achieve continuous security.</p>
<p><em><a href="https://www.safaribooksonline.com/library/view/amazon-web-services/9781617292880/?utm_source=oreilly&amp;utm_medium=newsite&amp;utm_campaign=10-top-aws-resources-on-oreillys-online-learning-platform_body_link"><strong>Amazon Web Services in Action</strong></a></em> — Michael Wittig and Andreas Wittig introduce you to computing, storing, and networking in the AWS cloud.</p>
<p><em><a href="https://www.safaribooksonline.com/videos/aws-cloudformation-master/9781789343694?utm_source=oreilly&amp;utm_medium=newsite&amp;utm_campaign=10-top-aws-resources-on-oreillys-online-learning-platform_body_link"><strong>AWS CloudFormation Master Class</strong></a></em> — Stéphane Maarek teaches you to write complete AWS CloudFormation templates using YAML and covers all the recent CloudFormation features.</p>
<p><em><a href="https://www.safaribooksonline.com/library/view/aws-lambda-in/9781617293719/?utm_source=oreilly&amp;utm_medium=newsite&amp;utm_campaign=10-top-aws-resources-on-oreillys-online-learning-platform_body_link"><strong>AWS Lambda in Action: Event-Driven Serverless A</strong></a></em><em><a href="https://www.safaribooksonline.com/library/view/aws-lambda-in/9781617293719/?utm_source=oreilly&amp;utm_medium=newsite&amp;utm_campaign=10-top-aws-resources-on-oreillys-online-learning-platform_body_link"><strong>pplications</strong></a></em> — Danilo Poccia offers an example-driven tutorial that teaches you how to build applications that use an event-driven approach on the back end.</p>
<p>Continue reading <a href='https://www.oreilly.com/ideas/10-top-aws-resources-on-oreillys-online-learning-platform'>10 top AWS resources on O’Reilly’s online learning platform.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/TAhI7wGQfME" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/P2S6UfLn2Io" height="1" width="1" alt=""/>https://www.oreilly.com/ideas/10-top-aws-resources-on-oreillys-online-learning-platformhttp://feedproxy.google.com/~r/oreilly/radar/atom/~3/TAhI7wGQfME/10-top-aws-resources-on-oreillys-online-learning-platformFour short links: 18 December 20182018-12-18T12:05:00Ztag:www.oreilly.com,2018-12-18:/ideas/four-short-links-18-december-2018<p><em>Singing AI, Content Signing, Data Rights, and Query Processing</em></p><ol>
<li>
<a href="https://soranews24.com/2018/12/17/revolutionary-a-i-voice-software-produces-incredible-vocals-that-sound-just-like-a-real-human/">AI Voices</a> -- marketing copy, but I can't find technical detail. The demos are worth checking out. The sprint to automated pop music generation has begun. <i>Not just limited to Japanese, as it is also capable of producing convincing Mandarin and even English voices for songs such as Adele’s "Rolling in the Deep" and Britney Spears’ "Everytime" on their official website.</i>
</li>
<li>
<a href="https://github.com/theupdateframework/notary">Notary</a> -- <i>publishers can sign their content offline using keys kept highly secure. Once the publisher is ready to make the content available, they can push their signed trusted collection to a Notary server. Consumers, having acquired the publisher's public key through a secure channel, can then communicate with any Notary server or (insecure) mirror, relying only on the publisher's key to determine the validity and integrity of the received content.</i>
</li>
<li>
<a href="https://www.technologyreview.com/s/612588/its-time-for-a-bill-of-data-rights/">It's Time for a Bill of Data Rights</a> (MIT TR) -- <i>this essay argues that “data ownership” is a flawed, counterproductive way of thinking about data. It not only does not fix existing problems, it creates new ones. Instead, we need a framework that gives people rights to stipulate how their data is used without requiring them to take ownership of it themselves.</i> (via <a href="https://boingboing.net/2018/12/17/sui-generis-regimes.html">Cory Doctorow</a>)</li>
<li>
<a href="https://github.com/Microsoft/trill">Trill</a> -- <i>a single-node query processor for temporal or streaming data</i>: open source from Microsoft. Described in <a href="https://azure.microsoft.com/en-in/blog/microsoft-open-sources-trill-to-deliver-insights-on-a-trillion-events-a-day/">this blog post</a>.</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-18-december-2018'>Four short links: 18 December 2018.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/ZPcptzCY9Zk" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/2IDvgnCoMwE" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-18-december-2018http://feedproxy.google.com/~r/oreilly/radar/atom/~3/ZPcptzCY9Zk/four-short-links-18-december-2018Four short links: 17 December 20182018-12-17T12:10:00Ztag:www.oreilly.com,2018-12-17:/ideas/four-short-links-17-december-2018<p><em>Open Source Licensing, Computer History, Serverless, and Wicked Problems</em></p><ol>
<li>
<a href="http://dtrace.org/blogs/bmc/2018/12/14/open-source-confronts-its-midlife-crisis/">Open Source Confronts Its Midlife Crisis</a> (Bryan Cantrill) -- <i>To be clear, the underlying problem is not the licensing, it’s that these companies don’t know how to make money—they want open source to be its own business model, and seeing that the cloud service providers have an entirely viable business model, they want a piece of the action.</i> Also see Bryan's followup: <a href="http://dtrace.org/blogs/bmc/2018/12/16/a-eula-in-foss-clothing/">A EULA in FOSS Clothing</a>: <i>You will notice that this looks nothing like any traditional source-based license—but it is exactly the kind of boilerplate that you find on EULAs, terms-of-service agreements, and other contracts that are being rammed down your throat.</i>
</li>
<li>
<a href="https://medium.com/a-computer-of-ones-own/">A Computer of One's Own</a> -- fantastic precis of the work of significant women in computing history.</li>
<li>
<a href="https://www.tbray.org/ongoing/When/201x/2018/12/09/Serverlessness">Serverlessness</a> (Tim Bray) -- Tim works in AWS's Serverless group and has been collecting what he's learned in his years building serverless infrastructure.</li>
<li>
<a href="http://www.morebeyond.co.za/why-we-suck-at-solving-wicked-problems/">Why We Suck at Solving Wicked Problems</a> -- this rings true with my experience.</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-17-december-2018'>Four short links: 17 December 2018.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/XC6FX5peco4" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/drPB4V_du1Y" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-17-december-2018http://feedproxy.google.com/~r/oreilly/radar/atom/~3/XC6FX5peco4/four-short-links-17-december-2018Four short links: 14 December 20182018-12-14T09:00:00Ztag:www.oreilly.com,2018-12-14:/ideas/four-short-links-14-december-2018<p><em>Satellite LoRaWAN, Bret Victor, State of AI, and Immutable Documentation</em></p><ol>
<li>
<a href="https://www.fleet.space/">Fleet</a> -- launched satellites as backhaul for LoRaWAN base station traffic.</li>
<li>
<a href="https://postlight.com/trackchanges/podcast/computing-is-everywhere">Computing is Everywhere</a> -- podcast episode with Bret Victor. Lots of interesting history and context to what he's up to at Dynamicland. (via <a href="https://twitter.com/ftrain/status/976204020032442373">Paul Ford</a>)</li>
<li>
<a href="http://cdn.aiindex.org/2018/AI%20Index%202018%20Annual%20Report.pdf">AI Index 2018 Report</a> (Stanford) -- think of it as the Mary Meeker report for AI.</li>
<li>
<a href="https://codeascraft.com/2018/10/10/etsys-experiment-with-immutable-documentation/">Etsy's Experiment with Immutable Documentation</a> -- <i>In trying to overcome the problem of staleness, the crucial observation is that how-docs typically change faster than why-docs do. Therefore the more how-docs are mixed in with why-docs in a doc page, the more likely the page is to go stale. We’ve leveraged this observation by creating an entirely separate system to hold our how-docs.</i>
</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-14-december-2018'>Four short links: 14 December 2018.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/gtM9v4NIy6k" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/mRIuzvHuEjM" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-14-december-2018http://feedproxy.google.com/~r/oreilly/radar/atom/~3/gtM9v4NIy6k/four-short-links-14-december-2018Four short links: 13 December 20182018-12-13T09:00:00Ztag:www.oreilly.com,2018-12-13:/ideas/four-short-links-13-december-2018<p><em>CS Ethics, Insect IoT, Glitch Showcase, and SQL Repos</em></p><ol>
<li>
<a href="http://embeddedethics.seas.harvard.edu/">Embedded Ethics</a> -- Harvard project that <i>integrates ethics modules into courses across the standard computer science curriculum.</i> Those modules are straightforward, online, and open access.</li>
<li>
<a href="http://livingiot.cs.washington.edu/">Living IOT: A Flying Wireless Platform on Live Insects</a> -- <i>We develop and deploy our platform on bumblebees which includes backscatter communication, low-power self-localization hardware, sensors, and a power source. We show that our platform is capable of sensing, backscattering data at 1 kbps when the insects are back at the hive, and localizing itself up to distances of 80 m from the access points, all within a total weight budget of 102 mg.</i> (via <a href="https://boingboing.net/2018/12/12/bees-wearing-wireless-sensors.html">BoingBoing</a>)</li>
<li>
<a href="https://glitch.com/culture/looky-what-we-made/">Looky What We Made</a> -- showcase of Glitch apps.</li>
<li>
<a href="https://caitlinhudon.com/2018/11/28/git-sql-together/">Git Your SQL Together</a> -- <i>why I recommend tracking SQL queries in git: 1. You will *always* need that query again. 2. Queries are living artifacts that change over time. 3. If it’s useful to you, it’s useful to others (and vice versa)</i>
</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-13-december-2018'>Four short links: 13 December 2018.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/4fLtXWdFz30" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/rB4d4invo-g" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-13-december-2018http://feedproxy.google.com/~r/oreilly/radar/atom/~3/4fLtXWdFz30/four-short-links-13-december-2018Four short links: 12 December 20182018-12-12T12:00:00Ztag:www.oreilly.com,2018-12-12:/ideas/four-short-links-12-december-2018<p><em>Render as Comic, Notebook to Production, Population Visualization, and Location Privacy</em></p><ol>
<li>
<a href="https://arxiv.org/pdf/1812.03473v1.pdf">Comixify</a> -- render video as comics.</li>
<li>
<a href="https://github.com/guillaume-chevalier/How-to-Grow-Neat-Software-Architecture-out-of-Jupyter-Notebooks">How to Grow Neat Software Architecture out of Jupyter Notebooks</a> -- everyone's coding in notebooks as a sweet step up from the basic one-command REPL loop. Here's some good advice on how to grow these projects without creating a spaghetti monster.</li>
<li>
<a href="https://pudding.cool/2018/10/city_3d/">City 3D</a> -- <i>This project wields data from the <a href="https://ghslsys.jrc.ec.europa.eu/index.php">Global Human Settlement Layer</a>, which uses “satellite imagery, census data, and volunteered geographic information” to create population density maps.</i> Best visualization I've seen in a very long time.</li>
<li>
<a href="https://www.nytimes.com/interactive/2018/12/10/business/location-data-privacy-apps.html">Your Apps Know Where You Were Last Night, and They're Not Keeping It Secret</a> (NY Times) -- <i>At least 75 companies receive anonymous, precise location data from apps whose users enable location services to get local news and weather or other information</i>. They claim 200M mobile devices, with updates as often as every six seconds. <i>These companies sell, use, or analyze the data to cater to advertisers, retail outlets, and even hedge funds seeking insights into consumer behavior. [...] An app may tell users that granting access to their location will help them get traffic information, but not mention that the data will be shared and sold. That disclosure is often buried in a vague privacy policy.</i>
</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-12-december-2018'>Four short links: 12 December 2018.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/yaPfZUfRo8I" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/MGTCJb5MWrQ" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-12-december-2018http://feedproxy.google.com/~r/oreilly/radar/atom/~3/yaPfZUfRo8I/four-short-links-12-december-2018Four short links: 11 December 20182018-12-11T19:25:00Ztag:www.oreilly.com,2018-12-11:/ideas/four-short-links-11-december-2018<p><em>Can We Stop?, Everything Breaks, Edge Cloud, and Molly Guard</em></p><ol>
<li>
<a href="https://medium.com/s/story/the-seductive-diversion-of-solving-bias-in-artificial-intelligence-890df5e5ef53">The Seductive Diversion of Solving Bias in Artificial Intelligence</a> -- provocative title, but the point is that <i>the preoccupation with narrow computational puzzles distracts us from the far more important issue of the colossal asymmetry between societal cost and private gain in the rollout of automated systems. It also denies us the possibility of asking: should we be building these systems at all?</i> The expected value of pursuing this line of thinking is pretty low because there's a vanishingly small probability that we can coordinate activity globally to prevent something bad from happening. Exhibit A: climate change.</li>
<li>
<a href="http://randsinrepose.com/archives/everything-breaks/">Everything Breaks</a> (Michael Lopp) -- <i>Humans will greatly benefit from a clear explanation of the rules of the game. The rules need to evolve in unexpected ways to account for the arrival of more humans. The only way to effectively learn to what is going to break is keeping playing...and learning.</i> See also <a href="https://stripe.com/atlas/guides/scaling-eng">lessons learned from scaling Stripe's engineering team</a>.</li>
<li>
<a href="https://wasm.fastlylabs.com/">Terrarium</a> (Fastly) -- an interesting glimpse at a possible future for web apps, where your CDN (which you need to have anyway if you're publishing anything remotely contentious or interesting) blurs with your hosting infrastructure provider. <i>Terrarium is a multi-language deployment platform based on WebAssembly. Think of it as a playground for experimenting with edge-side WebAssembly. Being one of the first Fastly Labs projects, you can also think of it as our way of publicly experimenting with what the future of real highly performant edge computing could look like.</i>
</li>
<li>
<a href="https://packages.debian.org/source/sid/molly-guard">molly-guard</a> -- <i>protects machines from accidental shutdowns/reboots</i>. <a href="https://en.wiktionary.org/wiki/molly-guard">Etymology of the name</a>: <i>originally a Plexiglas cover improvised for the Big Red Switch on an IBM 4341 mainframe after a programmer's toddler daughter (named Molly) tripped it twice in one day. Later generalized to covers over stop/reset switches on disk drives and networking equipment.</i> (via <a href="https://twitter.com/mikeforbes">Mike Forbes</a>)</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-11-december-2018'>Four short links: 11 December 2018.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/4niH96zZubI" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/mxaC99hlw_c" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-11-december-2018http://feedproxy.google.com/~r/oreilly/radar/atom/~3/4niH96zZubI/four-short-links-11-december-2018Four short links: 10 December 20182018-12-10T11:55:00Ztag:www.oreilly.com,2018-12-10:/ideas/four-short-links-10-december-2018<p><em>Language Zoo, VS AI, Advertising Plus, and Minecraft Scripting</em></p><ol>
<li>
<a href="http://plzoo.andrej.com/">The Programming Languages Zoo</a> -- <i>a collection of miniature programming languages that demonstrates various concepts and techniques used in programming language design and implementation.</i>
</li>
<li>
<a href="https://marketplace.visualstudio.com/items?itemName=VisualStudioExptTeam.vscodeintellicode">AI in Visual Studio Code</a> -- good to see IDEs getting AI-powered features to augment coders. In some small way, Doug Engelbart would be proud.</li>
<li>
<a href="https://a16z.com/2018/12/07/when-advertising-isnt-enough-multimodal-business-models-product-strategy/">Outgrowing Advertising: Multimodal Business Models as a Product Strategy</a> -- business models from Chinese companies that are augmenting advertising with other revenue streams.</li>
<li>
<a href="https://minecraft.net/en-us/article/scripting-api-now-public-beta">Minecraft Scripting API in Public Beta</a> -- <i>The Minecraft Script Engine uses the JavaScript language. Scripts can be written and bundled with Behaviour Packs to listen and respond to game events, get (and modify) data in components that entities have, and affect different parts of the game.</i>
</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-10-december-2018'>Four short links: 10 December 2018.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/SxU4vefJAQc" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/aUjyrntD9ac" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-10-december-2018http://feedproxy.google.com/~r/oreilly/radar/atom/~3/SxU4vefJAQc/four-short-links-10-december-2018Four short links: 7 December 20182018-12-07T11:45:00Ztag:www.oreilly.com,2018-12-07:/ideas/four-short-links-7-december-2018<p><em>Broken Feedback, Fake AI, Teaching with Jupyter, and Multiplayer Code UI</em></p><ol>
<li>
<a href="https://www.theatlantic.com/technology/archive/2018/11/why-ratings-and-feedback-forms-dont-work/575455/">Why Ratings and Feedback Forms Don't Work</a> (The Atlantic) -- <i>Negative feedback is actually good feedback because it yields greater efficiency and performance. [...] Positive feedback, by contrast, causes the system to keep going, unchecked. Like a thermostat that registers the room as too warm and cranks up the furnace, it’s generally meant to be avoided. But today’s understanding of feedback has reversed those terms.</i>
</li>
<li>
<a href="https://medium.com/@kcimc/how-to-recognize-fake-ai-generated-images-4d1f6f9a2842">How to Recognize Fake AI-Generated Images</a> -- worth remembering that researchers are in a war with these kinds of heuristics because if "straight hair looks like paint," then a researcher can get a paper out of fixing that.</li>
<li>
<a href="https://jupyter4edu.github.io/jupyter-edu-book/">Teaching and Learning with Jupyter</a> -- <i><a href="https://github.com/jupyter4edu/jupyter-edu-book">open</a> about Jupyter and its use in teaching and learning.</i>
</li>
<li>
<a href="https://repl.it/site/blog/multi">repl.it Multiplayer</a> -- <i>code with friends in the same editor, execute programs in the same interpreter, interact with the same terminal, chat in the IDE, edit files and share the same system resources, and ship applications from the same interface.</i>
</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-7-december-2018'>Four short links: 7 December 2018.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/B3DKTLAJSaA" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/qOL8iltsl54" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-7-december-2018http://feedproxy.google.com/~r/oreilly/radar/atom/~3/B3DKTLAJSaA/four-short-links-7-december-2018Assessing progress in automation technologies2018-12-06T17:09:00Ztag:www.oreilly.com,2018-12-06:/ideas/assessing-progress-in-automation-technologies<p><img src='https://d3ucjech6zwjp8.cloudfront.net/600x450/background-2009305_1920_crop-e2037effb3cc2b62c89efaead3b72dd6.jpg'/></p><p><em>When it comes to automation of existing tasks and workflows, you need not adopt an “all or nothing” attitude.</em></p><p>In this post, I share slides and notes from a <a href="https://www.oreilly.com/ideas/the-state-of-automation-technologies">keynote Roger Chen and I gave at the Artificial Intelligence conference in London</a> in October 2018. We presented an overview of the state of automation technologies: we tried to highlight the state of the key building block technologies and we described how these tools might evolve in the near future.</p>
<p>To assess the state of adoption of machine learning (ML) and AI, we recently conducted <a href="https://www.oreilly.com/data/free/state-of-machine-learning-adoption-in-the-enterprise.csp">a survey</a> that garnered more than 11,000 respondents. As I pointed out in previous posts, we learned many companies are still in the early stages of deploying machine learning:</p>
<figure class="center" id="id-6YOix"><img alt="machine learning adoption" src="https://d3ansictanv2wj.cloudfront.net/Figure1-2b4c459356780f5db284b8ab6337e134.jpg"></figure>
<p>Companies cite “lack of data” and “lack of skilled people” as the main factors holding back adoption. In many instances, “lack of data” is literally the state of affairs: companies have yet to collect and store the data needed to train the ML models they desire. The “skills gap” is real and persistent. Developers have taken heed of this growth in demand. In our own online learning platform, we are seeing strong growth in usage of content across AI topics, including 77% growth in consumption of content pertaining to deep learning:</p>
<figure class="center" id="id-RlWiz"><img alt="O'Reilly online learning platform deep learning content" src="https://d3ansictanv2wj.cloudfront.net/Figure2-113d0e70df4ac00ce38cc23c31da8841.jpg"></figure>
<p>We are also seeing strong growth in interest in new tools and topics such as PyTorch and reinforcement learning. In the case of reinforcement learning, <a href="https://www.oreilly.com/ideas/notes-from-the-first-ray-meetup">new tools like Ray are already spurring companies</a> to examine alternative solutions to <a href="https://www.oreilly.com/ideas/machine-learning-needs-machine-teaching">multi-step decision problems, where models might be hard to build</a> using supervised learning.</p>
<p>Decision-makers also are investing in AI and automation technologies. A <a href="https://www.hnkpmgciosurvey.com/">recent survey</a> of close to 4,000 IT leaders across 84 countries found that more companies are starting to invest in AI and automation technologies:</p>
<ul>
<li>The level of investment depends on the company. Companies that already consider themselves digital leaders tend to report a much higher level of investment in AI and automation.</li>
<li>Location also matters. Given the highly competitive business environment in China, it’s no surprise that companies there also tend to invest at a much higher rate. This aligns with a recent <a href="https://www.oreilly.com/ideas/china-ai-superpower">overview on AI in China delivered by Kai-Fu Lee</a> at our AI conference in San Francisco this past September.</li>
</ul>
<p>Progress in AI technologies has been fueled by the growth in data and improvements in compute and models. Let’s briefly examine each of these elements.</p>
<h2>Deep learning models</h2>
<p>Resurgence in deep learning began in 2011/2012 with record-setting models for speech recognition and computer vision. When I first began following deep learning in 2013, the community was small and tight-knit. Best practices were passed through internships in a few groups, and a lot of knowledge was <a href="http://radar.oreilly.com/2013/10/deep-learning-oral-traditions.html">shared in the form of “oral tradition.”</a> Today, the community is much larger.</p>
<figure class="center" id="id-RMkiq"><img alt="deep learning community" src="https://d3ansictanv2wj.cloudfront.net/Figure3-d82dcdbae461a33d175b7f424d619daa.jpg"></figure>
<p>Progress in research has been made possible by the steady improvement in: (1) data sets, (2) hardware and software tools, and (3) a culture of sharing and openness through conferences and websites like <a href="https://arxiv.org/">arXiv</a>. Novices and non-experts have also benefited from easy-to-use, open source libraries for machine learning.</p>
<figure class="center" id="id-3EaiZ"><img alt="machine learning libraries" src="https://d3ansictanv2wj.cloudfront.net/Figure4-064e78456cd73175176a791baef3aa00.jpg"></figure>
<p>These open source ML libraries have leveled the playing field and have made it possible for non-expert developers to build interesting applications. In fact, in 2017 we featured a couple of talented teenagers (Kavya Kopparapu and Abu Qader) at our AI conferences. They both were self-taught, and both were able to build potentially <a href="https://spectrum.ieee.org/the-human-os/biomedical/diagnostics/teenage-whiz-kid-invents-an-ai-system-to-diagnose-her-grandfathers-eye-disease">high-impact</a> prototypes <a href="https://www.npr.org/sections/goatsandsoda/2017/12/26/564457899/teenager-aims-to-improve-breast-cancer-diagnosis-in-poor-countries">involving deep learning</a>.</p>
<p>Companies have taken notice and want to build ML and AI into their systems and products. In 2015, LinkedIn ran a study and found that the U.S. had a national surplus of people with data science skills. <a href="https://economicgraph.linkedin.com/resources/linkedin-workforce-report-august-2018">That’s no longer the case today</a>:</p>
<ul>
<li>Demand in key metro areas in the U.S. is extremely high.</li>
<li>Cutting-edge skills like AI and machine learning will likely spread to other industries and geographies in the future.</li>
</ul>
<h2>Data</h2>
<p>With that said, having great models isn’t sufficient. At least for now, many of the models we rely on—including deep learning and reinforcement learning—are data hungry. Since they have the potential to scale to many, many users, <a href="https://www.wired.com/story/ai-and-enormous-data-could-make-tech-giants-harder-to-topple/">the largest companies in the largest countries have an advantage</a> over the rest of us. China, in particular, has been dubbed <a href="https://www.economist.com/business/2017/07/15/china-may-match-or-beat-america-in-ai">“the Saudi Arabia of data.”</a> Because AI research depends on having access to large data sets, we’re already seeing more cutting-edge research coming out of the large U.S. and Chinese companies. <a href="https://nips.cc/">NIPS</a> used to be a sleepy academic conference. Now it sells out within minutes, and we’re seeing more papers coming from large U.S. and Chinese companies.</p>
<p>The good news is that there are new tools that might help the rest of us gain access to more data. Services for generating labeled data sets are increasingly using AI technologies. The ones that rely on human labelers are beginning to use machine learning tools to help their human workers scale, improve their accuracy, and make training data more affordable. In certain domains, new tools like GANs and simulation platforms are able to provide realistic synthetic data that can be used to train machine learning models.</p>
<p>In addition to data generation, another important aspect is data sharing. There are also new startups <a href="https://www.computable.io/">building open source tools to improve data liquidity</a>. These startups are using tools like cryptography, blockchains, and secure communication to build data networks that enable organizations to share data securely.</p>
<h2>Compute</h2>
<p>Machine learning researchers are constantly exploring new algorithms. In the case of deep learning, this usually means trying new neural network architectures, refining parameters, or exploring new optimization techniques. As Turing Award winner David Patterson <a href="https://www.oreilly.com/ideas/a-new-golden-age-for-computer-architecture">describes</a> it, “The appetite for training is unlimited!”</p>
<p>The challenge is that experiments can take a long time to complete: hours, days, or even weeks. <a href="https://twitter.com/beenwrekt/status/961262527240921088">Computation also can cost a lot of money</a>. This means researchers cannot casually run such long and complex experiments, even if they had the patience to wait for them to finish.</p>
<p>We are in year seven of this renewed interest in AI and deep learning. At this stage, companies know the types of computations involved and they are beginning to see enough demand to justify building specialized hardware to accelerate those computations. Hardware companies, including our partner Intel, continue to release suites of hardware products for AI (including compute, memory, host bandwidth, and I/O bandwidth). The demand is so great that other companies—including ones that aren’t known for processors—are beginning to jump into the fray.</p>
<figure class="center" id="id-RkAiJ"><img alt="AI and deep learning innovation timeline" src="https://d3ansictanv2wj.cloudfront.net/Figure5-1a53d36dba9044c1a3c059f9231ef69c.jpg"></figure>
<p>More help is on the way. We see a lot of new companies working on specialized hardware. You have hardware for the data center, where the task of training large models using large data sets usually takes place. We are also entering an age where billions of edge devices will be expected to perform inference tasks, like image recognition. Hardware for these edge devices needs to be energy efficient and reasonably priced.</p>
<p>Numerous hardware startups are targeting deep learning both in China and in the U.S. The San Francisco Bay Area, in particular, is a hotbed for experienced hardware engineers and entrepreneurs, many of whom are working on AI-related startups. As you can see below, many hardware startups are targeting edge devices:</p>
<figure class="center" id="id-592iW"><img alt="AI and deep learning in startups" src="https://d3ansictanv2wj.cloudfront.net/Figure6-48bd92c3f621c419e2a1ee5518da3edc.jpg"></figure>
<h2>Closing thoughts</h2>
<p>We’ve talked about data, models, and compute mainly in the context of traditional performance measures: namely, optimizing machine learning or even business metrics. The reality is that there are many other considerations. For example, in certain domains (including health and finance) systems need to be explainable. Other aspects including fairness, privacy and security, and reliability and safety are also all important considerations as ML and AI get deployed more widely. This is a real concern for companies. In a <a href="https://www.oreilly.com/data/free/state-of-machine-learning-adoption-in-the-enterprise.csp">recent survey</a>, we found strong awareness and concern over these issues on the part of data scientists and data engineers.</p>
<p>Consider reliability and safety. While we can start building computer vision applications today, we need to remember that <a href="https://arxiv.org/abs/1808.03305">they can be brittle</a>. In certain domains, we will need to understand safety implications and we will need to prioritize reliability over efficiency gains provided by automation. The founders of Mobileye described it best: <a href="https://arxiv.org/abs/1708.06374">the main parameter in the race for autonomous cars cannot be who will have the first car on the road</a>.</p>
<p>Developing safe, explainable, fair, and secure AI applications will happen in stages. When it comes to automation of existing tasks and workflows, you need not adopt an “all or nothing” attitude. Many of these technologies can already be used for basic and partial automation of workflows.</p>
<figure class="center" id="id-6rJia"><img alt="evolution of AI assistance" src="https://d3ansictanv2wj.cloudfront.net/Figure7-0ffd4b81a4df3a76b1d3e0f0cbdc3658.jpg"></figure>
<p><strong>Related content:</strong></p>
<ul>
<li>Meredith Whittaker on <a href="https://www.oreilly.com/ideas/ai-foundations-what-shapes-the-ai-thats-shaping-our-world">“What shapes the AI that’s shaping our world?”</a>
</li>
<li><a href="https://www.oreilly.com/ideas/the-next-generation-of-ai-assistants-in-enterprise">“The next generation of AI assistants in enterprise”</a></li>
<li><a href="https://www.oreilly.com/ideas/how-to-think-about-ai-and-machine-learning-technologies-and-their-roles-in-automation">“How to think about AI and machine learning technologies, and their roles in automation”</a></li>
<li><a href="https://www.oreilly.com/ideas/what-machine-learning-means-for-software-development">“What machine learning means for software development”</a></li>
<li>Kai-Fu Lee on <a href="https://www.oreilly.com/ideas/china-ai-superpower">“China: AI superpower”</a>
</li>
<li>David Patterson on <a href="https://www.oreilly.com/ideas/a-new-golden-age-for-computer-architecture">“A new golden age for computer architecture”</a>
</li>
</ul>
<p>Continue reading <a href='https://www.oreilly.com/ideas/assessing-progress-in-automation-technologies'>Assessing progress in automation technologies.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/Es1NHSFay4I" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/OHLDEfVL6G8" height="1" width="1" alt=""/>Ben Loricahttps://www.oreilly.com/ideas/assessing-progress-in-automation-technologieshttp://feedproxy.google.com/~r/oreilly/radar/atom/~3/Es1NHSFay4I/assessing-progress-in-automation-technologiesTools for generating deep neural networks with efficient network architectures2018-12-06T13:05:00Ztag:www.oreilly.com,2018-12-06:/ideas/tools-for-generating-deep-neural-networks-with-efficient-network-architectures<p><img src='https://d3ucjech6zwjp8.cloudfront.net/600x450/photosynthesis-391447_1920_crop-90eefd0e8285acad8952e79316ff00d8.jpg'/></p><p><em>The O’Reilly Data Show Podcast: Alex Wong on building human-in-the-loop automation solutions for enterprise machine learning.</em></p><p>In this episode of the <a href="https://www.oreilly.com/ideas/topics/oreilly-data-show-podcast">Data Show</a>, I spoke with <a href="http://www.eng.uwaterloo.ca/~a28wong/">Alex Wong</a>, associate professor at the University of Waterloo, and co-founder of <a href="https://www.darwinai.ca/">DarwinAI</a>, a startup that uses AI to address foundational challenges with deep learning in the enterprise. As the use of machine learning and analytics become more widespread, we’re beginning to see tools that enable data scientists and data engineers to scale and tackle many more problems and maintain more systems. This includes automation tools for the many stages involved in data science, including data preparation, feature engineering, model selection, and hyperparameter tuning, as well as tools for data engineering and data operations.</p>
<p>Wong and his collaborators are building solutions for enterprises, including tools for <a href="https://arxiv.org/abs/1809.05989">generating efficient neural networks</a> and for the <a href="https://arxiv.org/abs/1806.05512">performance analysis</a> of networks deployed to edge devices. </p><p>Continue reading <a href='https://www.oreilly.com/ideas/tools-for-generating-deep-neural-networks-with-efficient-network-architectures'>Tools for generating deep neural networks with efficient network architectures.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/cfSr_NJPB_o" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/c0b5EwgTcLo" height="1" width="1" alt=""/>Ben Loricahttps://www.oreilly.com/ideas/tools-for-generating-deep-neural-networks-with-efficient-network-architectureshttp://feedproxy.google.com/~r/oreilly/radar/atom/~3/cfSr_NJPB_o/tools-for-generating-deep-neural-networks-with-efficient-network-architecturesFour short links: 6 December 20182018-12-06T12:15:00Ztag:www.oreilly.com,2018-12-06:/ideas/four-short-links-6-december-2018<p><em>Public Domain, Optimistic Sci-Fi, C64 Defrag, and Quantum Computing</em></p><ol>
<li>
<a href="https://creativecommons.org/2018/12/05/join-us-for-a-grand-re-opening-of-the-public-domain/">Re-Opening of the Public Domain</a> (Creative Commons) -- after years of legal extension of copyright terms, 2019 will be the first year in which new materials fall into the American public domain, and Creative Commons is throwing a bash at the Internet Archive.</li>
<li>
<a href="https://www.theverge.com/2018/12/5/18055980/better-worlds-science-fiction-short-stories-video">Better Worlds</a> (The Verge) -- <i>starting on January 14th, we’ll be publishing Better Worlds: 10 original fiction stories, five animated adaptations, and five audio adaptations by a diverse roster of science fiction authors who take a more optimistic view of what lies ahead in ways both large and small, fantastical and everyday.</i> Necessary! I heard a great interview with <a href="https://medium.com/conversations-with-tyler/tyler-cowen-robert-wiblin-stubborn-attachments-80000-hours-podcast-359aa62aa8ab">Tyler Cowen</a> where he said, <i>"you cannot live with pessimism, right? There’s also a notion that more optimism is a partially self-fulfilling prophecy. Believing pessimistic views might make them more likely to come about."</i> It is a fallacy to conflate optimism with naivete.</li>
<li>
<a href="https://www.pagetable.com/?p=978">A Disk Defragmenter for the Commodore 64</a> -- I don't know what's more insane: watching a great 40x25 homage to the classic Windows defrag progress screen or reading the bonkers BASIC code behind it.</li>
<li>
<a href="https://www.nap.edu/catalog/25196/quantum-computing-progress-and-prospects">Quantum Computing Progress and Prospects</a> -- <i>an introduction to the field, including the unique characteristics and constraints of the technology, and assesses the feasibility and implications of creating a functional quantum computer capable of addressing real-world problems. This report considers hardware and software requirements, quantum algorithms, drivers of advances in quantum computing and quantum devices, benchmarks associated with relevant use cases, the time and resources required, and how to assess the probability of success.</i> Separate the hype from the reality and develop a sense of the probability of different possible evolutionary paths for the technology.</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-6-december-2018'>Four short links: 6 December 2018.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/BxOyb12CrYc" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/2fU7S_X2_q8" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-6-december-2018http://feedproxy.google.com/~r/oreilly/radar/atom/~3/BxOyb12CrYc/four-short-links-6-december-2018Distributed systems: A quick and simple definition2018-12-06T11:00:00Ztag:www.oreilly.com,2018-12-06:/ideas/distributed-systems-a-quick-and-simple-definition<p><img src='https://d3ucjech6zwjp8.cloudfront.net/600x450/operations-antenna-crop-33a3c069ad23ae33441bb4509ea64f08.jpg'/></p><p><em>Get a basic understanding of distributed systems and then go deeper with recommended resources.</em></p><p>The technology landscape has evolved into an always-on environment of mobile, social, and cloud applications where programs can be accessed and used across a multitude of devices.</p>
<p>These always-on and always-available expectations are handled by distributed systems, which manage the inevitable fluctuations and failures of complex computing behind the scenes.</p>
<p>“The increasing criticality of these systems means that it is necessary for these online systems to be built for redundancy, fault tolerance, and high availability,” writes Brendan Burns, distinguished engineer at Microsoft, in <a href="https://www.safaribooksonline.com/library/view/designing-distributed-systems/9781491983638/"><em>Designing Distributed Systems</em></a>. “The confluence of these requirements has led to an order of magnitude increase in the number of distributed systems that need to be built.”</p>
<p>In <a href="https://www.safaribooksonline.com/videos/distributed-systems-in/9781491924914"><em>Distributed Systems in One Lesson</em></a>, developer relations leader and teacher Tim Berglund says a simple way to think about distributed systems is that they are a collection of independent computers that appears to its user as a single computer.</p>
<p>Virtually all modern software and applications built today are distributed systems of some sort, says <a href="https://samnewman.io/">Sam Newman</a>, director at Sam Newman &amp; Associates and author of <a href="https://www.safaribooksonline.com/library/view/building-microservices/9781491950340/"><em>Building Microservices</em></a>. Even a monolithic application talking to a database is a distributed system, he says, “just a very simple one.”</p>
<p>While those simple systems can technically be considered distributed, when engineers refer to distributed systems they’re typically talking about massively complex systems made up of many moving parts communicating with one another, with all of it appearing to an end-user as a single product, says Nora Jones, a senior software engineer at Netflix.</p>
<p>Think anything from, well, Netflix, to an online store like Amazon, to an instant messaging platform like WhatsApp, to a customer relationship management application like Salesforce, to Google’s search application. These systems require everything from login functionality, user profiles, recommendation engines, personalization, relational databases, object databases, content delivery networks, and numerous other components all served up cohesively to the user.</p>
<h2>Benefits of distributed systems</h2>
<p>These days, it’s not so much a question of why a team would use a distributed system, but rather <em>when</em> they should shift in that direction and <em>how</em> <em>distributed</em> the system needs to be, experts say. </p>
<p>Here are three inflection points—the need for scale, a more reliable system, and a more powerful system—when a technology team might consider using a distributed system.</p>
<p><strong>Horizontal Scalability</strong></p>
<p>Computing processes across a distributed system happen independently from one another, notes Berglund in <a href="https://www.safaribooksonline.com/videos/distributed-systems-in/9781491924914"><em>Distributed Systems in One Lesson</em></a>. This makes it easy to add nodes and functionality as needed. Distributed systems offer “the ability to massively scale computing power relatively inexpensively, enabling organizations to scale up their businesses to a global level in a way that was not possible even a decade ago,” write Chad Carson, cofounder of Pepperdata, and Sean Suchter, director of Istio at Google, in <a href="https://www.safaribooksonline.com/library/view/effective-multi-tenant-distributed/9781492042839/"><em>Effective Multi-Tenant Distributed Systems</em></a><em>.</em></p>
<p><strong>Reliability</strong></p>
<p>Distributed systems create a reliable experience for end users because they rely on “hundreds or thousands of relatively inexpensive computers to communicate with one another and work together, creating the outward appearance of a single, high-powered computer,” write Carson and Suchter. In a single-machine environment, if that machine fails then so too does the entire system. When computation is spread across numerous machines, there can be a failure at one node that doesn’t take the whole system down, writes Cindy Sridharan, distributed systems engineer, in <a href="https://www.safaribooksonline.com/library/view/distributed-systems-observability/9781492033431/"><em>Distributed Systems Observability</em></a>.</p>
<p><strong>Performance</strong></p>
<p>In <a href="https://www.safaribooksonline.com/library/view/designing-distributed-systems/9781491983638/"><em>Designing Distributed Systems</em></a>, Burns notes that a distributed system can handle tasks efficiently because work loads and requests are broken into pieces and spread over multiple computers. This work is completed in parallel and the results are returned and compiled back to a central location.</p>
<h2>The challenges of distributed systems</h2>
<p>While the benefits of creating distributed systems can be great for scaling and reliability, distributed systems also introduce complexity when it comes to design, construction, and debugging. Presently, most distributed systems are one-off bespoke solutions, writes Burns in <a href="https://www.safaribooksonline.com/library/view/designing-distributed-systems/9781491983638/"><em>Designing Distributed Systems</em></a>, making them difficult to troubleshoot when problems do arise.</p>
<p>Here are three of the most common challenges presented by distributed systems.</p>
<p><strong>Scheduling </strong></p>
<p>Because the work loads and jobs in a distributed system do not happen sequentially, there must be prioritization, note Carson and Suchter in <a href="https://www.safaribooksonline.com/library/view/effective-multi-tenant-distributed/9781492042839/"><em>Effective Multi-Tenant Distributed Systems</em></a>:</p>
<blockquote><p>One of the primary challenges in a distributed system is in scheduling jobs and their component processes. Computing power might be quite large, but it is always finite, and the distributed system must decide which jobs should be scheduled to run where and when, and the relative priority of those jobs. Even sophisticated distributed system schedulers have limitations that can lead to underutilization of cluster hardware, unpredictable job run times, or both.</p></blockquote>
<p>Take Amazon, for example. Amazon technology teams need to understand which aspects of the online store need to be called upon first to create a smooth user experience. Should the search bar be called before the navigation bar? Think of the many ways both small and large that Amazon makes online shopping as useful as possible for its users.</p>
<p><strong>Latency</strong></p>
<p>With such a complex interchange between hardware computing, software calls, and communication between those pieces over networks, latency can become a problem for users.</p>
<p>“The more widely distributed your system, the more latency between the constituents of your system becomes an issue,” says Newman. “As the volume of calls over the networks increases, the more you’ll start to see transient partitions and potentially have to deal with them.”</p>
<p>Over time, this can lead to technology teams needing to make tradeoffs around availability, consistency, and latency, Newman says.</p>
<p><strong>Performance monitoring and observability </strong></p>
<p>Failure is inevitable, says Nora Jones, when it comes to distributed systems. How a technology team manages and plans for failure so a customer hardly notices it is key. When distributed systems become complex, observability into the technology stack to understand those failures is an enormous challenge.</p>
<p>Carson and Suchter illustrate this challenge in <a href="https://www.safaribooksonline.com/library/view/effective-multi-tenant-distributed/9781492042839/"><em>Effective Multi-Tenant Distributed Systems</em></a>:</p>
<blockquote><p>Truly useful monitoring for multi-tenant distributed systems must track hardware usage metrics at a sufficient level of granularity for each interesting process on each node. Gathering, processing, and presenting this data for large clusters is a significant challenge, in terms of both systems engineering (to process and store the data efficiently and in a scalable fashion) and the presentation-level logic and math (to present it usefully and accurately). Even for limited, node-level metrics, traditional monitoring systems do not scale well on large clusters of hundreds to thousands of nodes.</p></blockquote>
<p>There are several approaches companies can use to detect those failure points, such as distributed tracing, chaos engineering, incident reviews, and understanding expectations of upstream and downstream dependencies. “There’s a lot of different tactics to achieve high quality and robustness, and they all fit into the category of having as much insight into the system as possible,” Jones says.</p>
<h2>Learn more</h2>
<p>Ready to go deeper into distributed systems? Check out these recommended resources from O’Reilly’s editors.</p>
<p><a href="https://www.safaribooksonline.com/library/view/distributed-systems-observability/9781492033431/"><em>Distributed Systems Observability</em></a> — Cindy Sridharan provides an overview of monitoring challenges and trade-offs that will help you choose the best observability strategy for your distributed system.</p>
<p><a href="https://www.safaribooksonline.com/library/view/designing-distributed-systems/9781491983638/"><em>Designing Distributed Systems</em></a> — Brendan Burns demonstrates how you can adapt existing software design patterns for designing and building reliable distributed applications.</p>
<p><a href="https://www.safaribooksonline.com/videos/the-distributed-systems/9781491968383"><em>The Distributed Systems Video Collection</em></a> — This 12-video collection dives into best practices and the future of distributed systems.</p>
<p><a href="https://www.safaribooksonline.com/library/view/effective-multi-tenant-distributed/9781492042839/"><em>Effective Multi-Tenant Distributed Systems</em></a> — Chad Carson and Sean Suchter outline the performance challenges of running multi-tenant distributed computing environments, especially within a Hadoop context.</p>
<p><a href="https://www.safaribooksonline.com/videos/distributed-systems-in/9781491924914"><em>Distributed Systems in One Lesson</em></a> — Using a series of examples taken from a fictional coffee shop business, Tim Berglund helps you explore five key areas of distributed systems.</p>
<p><a href="https://www.oreilly.com/library/view/chaos-engineering/9781491988459/"><em>Chaos Engineering</em></a> — This report introduces you to Chaos Engineering, a method of experimenting on infrastructure that lets you expose weaknesses before they become problems.</p>
<p><a href="https://www.safaribooksonline.com/library/view/designing-data-intensive-applications/9781491903063/"><em>Designing Data-Intensive Applications</em></a> — Martin Kleppmann examines the pros and cons of various technologies for processing and storing data.</p>
<p>Continue reading <a href='https://www.oreilly.com/ideas/distributed-systems-a-quick-and-simple-definition'>Distributed systems: A quick and simple definition.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/9wKfgtWohSo" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/t-RZ1eIZCqI" height="1" width="1" alt=""/>James Furbushhttps://www.oreilly.com/ideas/distributed-systems-a-quick-and-simple-definitionhttp://feedproxy.google.com/~r/oreilly/radar/atom/~3/9wKfgtWohSo/distributed-systems-a-quick-and-simple-definitionFour short links: 5 December 20182018-12-05T12:00:00Ztag:www.oreilly.com,2018-12-05:/ideas/four-short-links-5-december-2018<p><em>NLP for Code, Monolith vs. Modular, Automatic Gender Recognition, and Budget Simulator</em></p><ol>
<li>
<a href="https://code2vec.org/">code2vec</a> -- <i>a dedicated website for demonstrating the principles shown in the paper <a href="https://arxiv.org/abs/1803.09473">code2vec: Learning Distributed Representations of Code</a></i>. An interesting start to using a productive NLP technique on code.</li>
<li>
<a href="https://staltz.com/two-schools-of-thought-in-open-source.html">Monolithic or Modular</a> -- <i>When monolithic adherents look at a modular project, they may think that it’s low quality or abandoned simply because commit count is low and rare, new features are not being added, and the project has no funding or community events. Interestingly, these same properties are what modular adherents will perceive as a good thing, likely to indicate that the module is complete. Monolithic adherents don’t believe a project could ever be “complete.”</i>
</li>
<li>
<a href="https://ironholds.org/resources/papers/agr_paper.pdf">The Misgendering Machines: Trans/HCI Implications of Automatic Gender Recognition</a> -- <i>I show that AGR consistently operationalizes gender in a trans-exclusive way, and consequently carries disproportionate risk for trans people subject to it. In addition, I use the dearth of discussion of this in HCI papers that apply AGR to discuss how HCI operationalizes gender and the implications that this has for the field’s research. I conclude with recommendations for alternatives to AGR and some ideas for how HCI can work toward a more effective and trans-inclusive treatment of gender.</i> (via <a href="https://twitter.com/old_sound/status/1069712505839255552">Alvaro Videla</a>)</li>
<li>
<a href="https://zarkonnen.itch.io/occult-defence-agency-budgeting-simulator">Occult Defence Agency Budgeting Simulator</a> -- a hilarious exercise whose point is about what happens the year after you cut the budget, with parallels to UK fiscal policy left as exercise for the (pixie-ravaged) reader. I've long held that simulations are a fantastic way to make a point. (via <a href="https://twitter.com/zarkonnen_com/status/1069956285892841474">David Stark</a>)</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-5-december-2018'>Four short links: 5 December 2018.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/X8rbut3xUYI" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/4WL87SYPoUY" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-5-december-2018http://feedproxy.google.com/~r/oreilly/radar/atom/~3/X8rbut3xUYI/four-short-links-5-december-2018Survey reveals the opportunities and realities of microservices2018-12-04T14:00:00Ztag:www.oreilly.com,2018-12-04:/ideas/survey-reveals-the-opportunities-and-realities-of-microservices<p><img src='https://d3ucjech6zwjp8.cloudfront.net/600x450/chuttersnap-300060-unsplash_crop-49c3db89aedeff126fc8c8fafbcb03d4.jpg'/></p><p><em>A new report explores how far companies have come with microservices.</em></p><p>Fads come and go in the technology world—anyone remember <a href="https://en.wikipedia.org/wiki/Ajax_(programming)">AJAX</a>? When new, shiny things appear, architects often struggle to determine whether this is merely the latest fad or a genuine future direction.</p>
<p>Microservices are evolving from fad to trend. Several years ago, many companies experimented with microservices but had doubts about the operational complexity and engineering maturity required to achieve success. However, enough companies tamed the dragons to realize real benefits, making this architectural style the prevailing trend in many industries for both new application development and the migration target for many existing systems.</p>
<p>The <a href="https://conferences.oreilly.com/software-architecture">O'Reilly Software Architecture Conference</a> tracks microservices, and we periodically check in with practitioners to see how it’s being implemented in the real world. O’Reilly conducted a survey on microservices maturity in July 2018 that aimed to assess how far companies have come with microservices, what challenges they face, and some common best practices. The 866 responses were summarized and analyzed in our free report, <a href="https://www.oreilly.com/programming/free/the-state-of-microservices-maturity.csp"><em>The State of Microservices Maturity</em></a>.</p>
<p>Insights from the report include:</p>
<ul>
<li>Containers continue to rise in popularity for microservices: 69% of survey respondents use containers for microservices deployment.</li>
<li>Although Kubernetes enjoys great popularity in the press and at conferences, adoption is still below the 40% mark for our survey respondents.</li>
<li>More than 50% of respondents use continuous deployment, which speaks to overall engineering maturity in the industry.</li>
<li>86% of respondents rate their microservices efforts at least partially successful.</li>
</ul>
<p>For the full survey findings and analysis, download <a href="https://www.oreilly.com/programming/free/the-state-of-microservices-maturity.csp"><em>The State of Microservices Maturity</em></a>.</p>
<p>Continue reading <a href='https://www.oreilly.com/ideas/survey-reveals-the-opportunities-and-realities-of-microservices'>Survey reveals the opportunities and realities of microservices.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/HMKEOXoFj7I" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/t-WCY7lbXlA" height="1" width="1" alt=""/>https://www.oreilly.com/ideas/survey-reveals-the-opportunities-and-realities-of-microserviceshttp://feedproxy.google.com/~r/oreilly/radar/atom/~3/HMKEOXoFj7I/survey-reveals-the-opportunities-and-realities-of-microservicesFour short links: 4 December 20182018-12-04T11:35:00Ztag:www.oreilly.com,2018-12-04:/ideas/four-short-links-4-december-2018<p><em>Voice Technology, AI Summaries, Time Tracker, and Homomorphic Encryption</em></p><ol>
<li>
<a href="https://medium.com/@nicolehe/fifteen-unconventional-uses-of-voice-technology-fa1b749c14bf">Fifteen Unconventional Uses of Voice Technology</a> (Nicole He) -- <i>Students had half a semester to learn tools like the Web Speech API, Dialogflow, and Actions on Google, and then were tasked with making something...interesting. The in-class code examples we used are on GitHub. Here are 15 funny, subversive, and impressively weird final projects from the class.</i>
</li>
<li>
<a href="https://www.topbots.com/most-important-ai-research-papers-2018/">Summary of 2018's Most Important AI Papers</a> -- <i>To help you catch up, we’ve summarized 10 important AI research papers from 2018 to give you a broad overview of machine learning advancements this year. There are many more breakthrough papers worth reading as well, but we think this is a good list for you to start with.</i>
</li>
<li>
<a href="http://arbtt.nomeata.de">arbtt</a> -- a time tracker that sits in the background. You write rules that tell it how to categorize your activity.</li>
<li>
<a href="https://github.com/Microsoft/SEAL">Microsoft Simple Encrypted Arithmetic Library</a> -- <i>an easy-to-use but powerful homomorphic encryption library written in C++. It supports both the BFV and the CKKS encryption schemes.</i> (via <a href="https://www.microsoft.com/en-us/research/blog/the-microsoft-simple-encrypted-arithmetic-library-goes-open-source/">Microsoft Research Blog</a>)</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-4-december-2018'>Four short links: 4 December 2018.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/r06GxRTrSFg" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/XjKpwRUxCiU" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-4-december-2018http://feedproxy.google.com/~r/oreilly/radar/atom/~3/r06GxRTrSFg/four-short-links-4-december-2018Four short links: 3 December 20182018-12-03T11:45:00Ztag:www.oreilly.com,2018-12-03:/ideas/four-short-links-3-december-2018<p><em>Amazon and OSS, Audio to Keystrokes, The New OS, and Software Sprawl</em></p><ol>
<li>
<a href="https://www.cnbc.com/2018/11/30/aws-is-competing-with-its-customers.html">Amazon is Competing with Its Customers</a> -- <i>What's more, Kreps said, Amazon has not contributed a single line of code to the Apache Kafka open source software and is not reselling Confluent's cloud tool.</i> Sometimes Amazon contributes back, but increasingly often it seems like its software MO is exploitation not co-creation. This is what prompted the creation of various "open except if you resell it as a cloud service"-source licenses, like the Commons Clause.</li>
<li>
<a href="https://github.com/ggerganov/kbd-audio">kbd-audio</a> -- <i>tools for capturing and analyzing keyboard input paired with microphone capture.</i>
</li>
<li>
<a href="https://www.infoworld.com/article/3322120/kubernetes/sorry-linux-kubernetes-is-now-the-os-that-matters.html">Kubernetes is the OS That Matters</a> (Matt Asay) -- provocative clickbait title, but the point is important: if single-machine apps are the exception, then the lowest layer of critical shared software is no longer the OS but instead the cluster manager.</li>
<li>
<a href="https://charity.wtf/2018/12/02/software-sprawl-the-golden-path-and-scaling-teams-with-agency/">Software Sprawl, The Golden Path, and Scaling Teams with Agency</a> (Charity Majors) -- good talk on how to recover from "we're using too many shiny tools, and it's hard to make progress because there's no common set of tools, so everyone's reinventing the wheel, and omg fire."</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-3-december-2018'>Four short links: 3 December 2018.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/KemPa-yjomU" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/vkWExPXNuDs" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-3-december-2018http://feedproxy.google.com/~r/oreilly/radar/atom/~3/KemPa-yjomU/four-short-links-3-december-2018Four short links: 30 November 20182018-11-30T12:00:00Ztag:www.oreilly.com,2018-11-30:/ideas/four-short-links-30-november-2018<p><em>Advents are Coming, Open Source, Restricted Exports, and Misinformation Operations</em></p><ol>
<li>
<a href="http://www.qemu-advent-calendar.org/2018/">QEMU Advent Calendar</a> -- <i>An amazing QEMU disk image every day!</i>. It's that time of year again! See also <a href="https://adventofcode.com/2018">Advent of Code</a>.</li>
<li>
<a href="https://13brane.net/rants/de-facto-closed-source/">De Facto Closed Source</a> -- <i>You want to download thousands of lines of useful, but random, code from the internet, for free, run it in a production web server, or worse, your user’s machine, trust it with your paying users’ data and reap that sweet dough. We all do. But then you can’t be bothered to check the license, understand the software you are running, and still want to blame the people who make your business a possibility when mistakes happen, while giving them nothing for it? This is both incompetence and entitlement.</i>
</li>
<li>
<a href="https://www.gpo.gov/fdsys/pkg/FR-2018-11-19/pdf/2018-25221.pdf">U.S. Government Wonders What to Limit Exports Of</a> -- <i>The representative general categories of technology for which Commerce currently seeks to determine whether there are specific emerging technologies that are essential to the national security of the United States include: (1) Biotechnology, such as: (i) Nanobiology; (ii) Synthetic biology; (iv) Genomic and genetic engineering; or (v) Neurotech. (2) Artificial intelligence (AI) and machine learning technology, such as: (i) Neural networks and deep learning (e.g., brain modeling, time series prediction, classification); (ii) Evolution and genetic computation (e.g., genetic algorithms, genetic programming); (iii) Reinforcement learning; (iv) Computer vision (e.g., object recognition, image understanding); (v) Expert systems (e.g., decision support systems, teaching systems); (vi) Speech and audio processing (e.g., speech recognition and production); (vii) Natural language processing (e.g., machine translation); (viii) Planning (e.g., scheduling, game playing); (ix) Audio and video manipulation technologies (e.g., voice cloning, deepfakes); (x) AI cloud technologies; or (xi) AI chipsets. (3) Position, Navigation, and Timing (PNT) technology. (4) Microprocessor technology, such as: (i) Systems-on-Chip (SoC); or (ii) Stacked Memory on Chip. (5) Advanced computing technology, such as: (i) Memory-centric logic. (6) Data analytics technology, such as: (i) Visualization; (ii) Automated analysis algorithms; or (iii) Context-aware computing. (7) Quantum information and sensing technology, such as (i) Quantum computing; (ii) Quantum encryption; or (iii) Quantum sensing. (8) Logistics technology, such as: (i) Mobile electric power; (ii) Modeling and simulation; (iii) Total asset visibility; or (iv) Distribution-based Logistics Systems (DBLS). (9) Additive manufacturing (e.g., 3D printing); (10) Robotics such as: (i) Micro-drone and micro-robotic systems; (ii) Swarming technology; (iii) Self-assembling robots; (iv) Molecular robotics; (v) Robot compliers; or (vi) Smart Dust. (11) Brain-computer interfaces, such as (i) Neural-controlled interfaces; (ii) Mind-machine interfaces; (iii) Direct neural interfaces; or (iv) Brain-machine interfaces. (12) Hypersonics, such as: (i) Flight control algorithms; (ii) Propulsion technologies; (iii) Thermal protection systems; or (iv) Specialized materials (for structures, sensors, etc.). (13) Advanced Materials, such as: (i) Adaptive camouflage; (ii) Functional textiles (e.g., advanced fiber and fabric technology); or (iii) Biomaterials. (14) Advanced surveillance technologies, such as: Faceprint and voiceprint technologies.</i> It's a great list of what's in the next Gartner Hype Cycle report.</li>
<li>
<a href="https://www.ribbonfarm.com/2018/11/28/the-digital-maginot-line/">The Digital Maginot Line</a> (Renee DiResta) -- <i>We know this is coming, and yet we’re doing very little to get ahead of it. No one is responsible for getting ahead of it. [...] platforms aren’t incentivized to engage in the profoundly complex arms race against the worst actors when they can simply point to transparency reports showing that they caught a fair number of the mediocre actors. [...] The regulators, meanwhile, have to avoid the temptation of quick wins on meaningless tactical bills (like the Bot Law) and wrestle instead with the longer-term problems of incentivizing the platforms to take on the worst offenders (oversight), and of developing a modern-day information operations doctrine.</i>
</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-30-november-2018'>Four short links: 30 November 2018.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/6R5rQyjlYCY" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/X_ZWAwKUTR8" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-30-november-2018http://feedproxy.google.com/~r/oreilly/radar/atom/~3/6R5rQyjlYCY/four-short-links-30-november-2018Four short links: 29 November 20182018-11-29T12:00:00Ztag:www.oreilly.com,2018-11-29:/ideas/four-short-links-29-november-2018<p><em>Security Sci-Fi, AWS Toys, Quantum Ledger, and Insecurity in Software in Hardware</em></p><ol>
<li>
<a href="https://cliffnest.shortinfosec.net/toc">The Cliff Nest</a> -- sci-fi story with computer security challenges built in.</li>
<li>
<a href="https://aws.amazon.com/textract/">Amazon Textract</a> -- OCR in the cloud, extracting not just text but also structured tables. Part of a big feature dump Amazon's done today, including <a href="https://aws.amazon.com/blogs/aws/amazon-personalize-real-time-personalization-and-recommendation-for-everyone/">recommendations</a>, <a href="https://aws.amazon.com/outposts/">AWS on-prem</a>, and <a href="https://aws.amazon.com/timestream/">a fully managed time series database</a>.</li>
<li>
<a href="https://aws.amazon.com/qldb/">Quantum Ledger Database</a> -- <i>a fully managed ledger database that provides a transparent, immutable, and cryptographically verifiable transaction log owned by a central trusted authority. Amazon QLDB tracks each and every application data change and maintains a complete and verifiable history of changes over time.</i> Many of the advantages of a blockchain ledger without the distributed pains. Quantum in the sense of "minimum chunk of something," not "uses quantum computing."</li>
<li>
<a href="https://www.bleepingcomputer.com/news/security/sennheiser-headset-software-could-allow-man-in-the-middle-ssl-attacks/">Sennheiser Headset Software Enabled MITM Attacks</a> -- <i>When users have been installing Sennheiser's HeadSetup software, little did they know the software was also installing a root certificate into the Trusted Root CA Certificate store. To make matters worse, the software was also installing an encrypted version of the certificate's private key that was not as secure as the developers may have thought.</i> This is the price of using software to improve hardware.</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-29-november-2018'>Four short links: 29 November 2018.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/7Fnsl_QlVZ0" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/QfVgEDe-4S0" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-29-november-2018http://feedproxy.google.com/~r/oreilly/radar/atom/~3/7Fnsl_QlVZ0/four-short-links-29-november-2018Four short links: 28 November 20182018-11-28T12:00:00Ztag:www.oreilly.com,2018-11-28:/ideas/four-short-links-28-november-2018<p><em>FaaS, Space as a Service, Bot Yourself, and Facebook's RL Platform</em></p><ol>
<li>
<a href="https://firecracker-microvm.github.io/">Firecracker</a> -- Amazon's <i>open source virtualization technology that is purpose-built for creating and managing secure, multitenant containers and functions-based services.</i> Docker but for FaaS platforms. Best explanation is <a href="https://lobste.rs/s/vtocd6/aws_firecracker_secure_fast_microvms_for#c_16or70">on lobste.rs</a>: <i>Firecracker is solving the problem of multitenant container density while maintaining the security boundary of a VM. If you’re entirely running first-party trusted workloads and are satisfied with them all sharing a single kernel and using Linux security features like cgroups, selinux, and seccomp, then Firecracker may not be the best answer. If you’re running workloads from customers similar to Lambda, desire stronger isolation than those technologies provide, or want defense in depth, then Firecracker makes a lot of sense. It can also make sense if you need to run a mix of different Linux kernel versions for your containers and don’t want to spend a whole bare-metal host on each one.</i>
</li>
<li>
<a href="https://aws.amazon.com/blogs/aws/aws-ground-station-ingest-and-process-data-from-orbiting-satellites/">Amazon Ground Station: Ingest and Process Data from Orbiting Satellites</a> -- a sign that space is becoming more mainstream. Also interesting because they're doing a bunch of processing in EC2 rather than at the basestation. General-purpose computers often beat specialized ones.</li>
<li>
<a href="https://github.com/Spandan-Madan/Me_Bot">Me Bot</a> -- <i>A simple tool to make a bot that speaks like you, simply learning from your WhatsApp Chats.</i> (via <a href="https://news.ycombinator.com/item?id=18540125">Hacker News</a>)</li>
<li>
<a href="https://code.fb.com/ml-applications/horizon/">Horizon</a> -- FB open sources <i>reinforcement learning platform for large-scale products and services</i>, built on PyTorch.</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-28-november-2018'>Four short links: 28 November 2018.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/bnOgG1x8ZbE" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/XNNoGTuxUSI" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-28-november-2018http://feedproxy.google.com/~r/oreilly/radar/atom/~3/bnOgG1x8ZbE/four-short-links-28-november-2018Four short links: 27 November 20182018-11-27T11:55:00Ztag:www.oreilly.com,2018-11-27:/ideas/four-short-links-27-november-2018<p><em>Open Source, Interactive Fiction, Evolving Images, and Closed Worlds</em></p><ol>
<li>
<a href="https://gist.github.com/richhickey/1563cddea1002958f96e7ba9519972d9">Open Source is Not About You</a> (Rich Hickey) -- <i>As a user of something open source, you are not thereby entitled to anything at all. You are not entitled to contribute. You are not entitled to features. You are not entitled to the attention of others. You are not entitled to having value attached to your complaints. You are not entitled to this explanation.</i> Tough love talk. See also <a href="https://gist.github.com/dominictarr/9fd9c1024c94592bc7268d36b8d83b3a">this statement</a> by the author of the event-stream NPM module, who passed maintenance onto someone who added malware to it. <i>If it's not fun anymore, you get literally nothing from maintaining a popular package.</i>
</li>
<li>
<a href="https://ganbreeder.app/">Ganbreeder</a> -- explore images created by generative adversarial networks.</li>
<li>
<a href="https://ifcomp.org/comp/2018">2018 IFComp Winners</a> -- interactive fiction is nextgen chatbot tech. Worth keeping up with to see how they stretch parsers and defy expectations of the genre.</li>
<li>
<a href="http://we-make-money-not-art.com/the-architecture-of-closed-worlds-or-what-is-the-power-of-shit/">The Architecture of Closed Worlds</a> (We Make Money Not Art) -- <i>One of the most striking lessons of the book is that it is extremely difficult to create a miniaturized world without inheriting some of the problems of the surrounding world. No matter how much control was exerted on the synthetic habitats, no matter how ambitious the vision, the breadth of engineering and human ingeniosity, the results were marred by surprisingly mundane obstacles: gerbils outsmarting the machine, bacteria loss, fingernails and skin infiltrating collectors, or simply the difficulty of implementing behavioural changes.</i> The physical version of online social networks that are shocked to discover their userbase includes pedophiles, racists, stalkers, murderers, nutters, and malicious folks.</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-27-november-2018'>Four short links: 27 November 2018.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/uJTxlLNn6vU" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/JTZ1Pn89OPA" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-27-november-2018http://feedproxy.google.com/~r/oreilly/radar/atom/~3/uJTxlLNn6vU/four-short-links-27-november-2018Four short links: 26 November 20182018-11-26T12:35:00Ztag:www.oreilly.com,2018-11-26:/ideas/four-short-links-26-november-2018<p><em>Graphics Engine, Graph Library, Docker Tool, and Probabilistic Cognition</em></p><ol>
<li>
<a href="https://heaps.io/">Heaps</a> -- <i>a mature cross-platform graphics engine designed for high-performance games. It is designed to leverage modern GPUs that are commonly available on both desktop and mobile devices.</i> 2D and 3D game framework, built on the Haxe language and toolkit.</li>
<li>
<a href="https://github.com/anvaka/VivaGraphJS">VivaGraphJS</a> -- JavaScript graph manipulation and rendering in JavaScript, <i>designed to be extensible and to support different rendering engines and layout algorithms.</i>
</li>
<li>
<a href="https://github.com/wagoodman/dive">dive</a> -- <i>tool for exploring each layer in a docker image</i>.</li>
<li>
<a href="http://probmods.org/">Probabilistic Models of Cognition</a> -- <i>This book explores the probabilistic approach to cognitive science, which models learning and reasoning as inference in complex probabilistic models. We examine how a broad range of empirical phenomena, including intuitive physics, concept learning, causal reasoning, social cognition, and language understanding, can be modeled using probabilistic programs (using the WebPPL language).</i>
</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-26-november-2018'>Four short links: 26 November 2018.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/DogpxjHuEg8" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/4ixskIS9RGE" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-26-november-2018http://feedproxy.google.com/~r/oreilly/radar/atom/~3/DogpxjHuEg8/four-short-links-26-november-2018Four short links: 23 November 20182018-11-23T13:20:00Ztag:www.oreilly.com,2018-11-23:/ideas/four-short-links-23-november-2018<p><em>Chinese iPhone Users, Sci-Fi UI, MITM Framework, and HTTP/3</em></p><ol>
<li>
<a href="https://www.scmp.com/tech/article/2174310/research-highlights-class-divide-between-poor-apple-iphone-and-rich-huawei">Chinese iPhone Users are Poor</a> -- <i>The Shanghai-based firm also found that most iPhone users are unmarried females aged between 18 and 34, who graduated with just a high school certificate and earn a monthly income of below 3,000 yuan (HK$3,800). They are perceived to be part of a group known as the “invisible poor”—those who do not look as poor as their financial circumstances.</i>
</li>
<li>
<a href="https://github.com/GitSquared/edex-ui">eDEX-UI</a> -- <i>a fullscreen desktop application resembling a sci-fi computer interface, heavily inspired from DEX-UI and the TRON Legacy movie effects. It runs the shell of your choice in a real terminal and displays live information about your system. It was made to be used on large touchscreens but will work nicely on a regular desktop computer or perhaps a tablet PC or one of those funky 360° laptops with touchscreens.</i>
</li>
<li>
<a href="https://github.com/kgretzky/evilginx2">evilginx2</a> -- <i>a man-in-the-middle attack framework used for phishing login credentials along with session cookies, which in turn allows one to bypass 2-factor authentication protection.</i>
</li>
<li>
<a href="https://blog.erratasec.com/2018/11/some-notes-about-http3.html">Some Notes About HTTP/3</a> (Errata Security) -- <i>QUIC is really more of a new version of TCP (TCP/2???) than a new version of HTTP (HTTP/3). It doesn't really change what HTTP/2 does so much as change how the transport works. Therefore, my comments below are focused on transport issues rather than HTTP issues.</i>
</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-23-november-2018'>Four short links: 23 November 2018.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/IXPDPbdNVKY" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/H17AQ1xYqP0" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-23-november-2018http://feedproxy.google.com/~r/oreilly/radar/atom/~3/IXPDPbdNVKY/four-short-links-23-november-2018Four short links: 22 November 20182018-11-22T13:50:00Ztag:www.oreilly.com,2018-11-22:/ideas/four-short-links-22-november-2018<p><em>XOXO Talks, Git Illustrated, Post-REST Services, and Learning Projects</em></p><ol>
<li>
<a href="https://www.youtube.com/playlist?list=PLCbA9r6ecYWVwo9f5Ro_JuKEBwS8kugzP">XOXO 2018 Videos</a> -- playlist of talks from XOXO 2018. (via <a href="https://boingboing.net/2018/11/21/videos-from-this-years-xoxo.html">BoingBoing</a>)</li>
<li>
<a href="https://learngitbranching.js.org/">Learn Git Branching</a> -- visual!</li>
<li>
<a href="https://www.tbray.org/ongoing/When/201x/2018/11/18/Post-REST">Post-REST</a> (Tim Bray) -- musings on what might replace REST in different parts of the current world of web services.</li>
<li>
<a href="https://github.com/karan/Projects">Projects</a> -- <i>list of practical projects that anyone can solve in any programming language</i>, divided into categories according to what the project will exercise your knowledge of—e.g., <i>Files</i>, <i>Data Structures</i>, <i>Threading</i>, etc. Good for teachers looking for ideas.</li>
</ol>
<p>Continue reading <a href='https://www.oreilly.com/ideas/four-short-links-22-november-2018'>Four short links: 22 November 2018.</a></p><img src="http://feeds.feedburner.com/~r/oreilly/radar/atom/~4/c4mTq0Q1YGk" height="1" width="1" alt=""/><img src="http://feeds.feedburner.com/~r/oreilly/radar/rss10/~4/ETZFMQmUn6k" height="1" width="1" alt=""/>Nat Torkingtonhttps://www.oreilly.com/ideas/four-short-links-22-november-2018http://feedproxy.google.com/~r/oreilly/radar/atom/~3/c4mTq0Q1YGk/four-short-links-22-november-2018